Search results for: Semantic features
1558 Personal Authentication Using FDOST in Finger Knuckle-Print Biometrics
Authors: N. B. Mahesh Kumar, K. Premalatha
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The inherent skin patterns created at the joints in the finger exterior are referred as finger knuckle-print. It is exploited to identify a person in a unique manner because the finger knuckle print is greatly affluent in textures. In biometric system, the region of interest is utilized for the feature extraction algorithm. In this paper, local and global features are extracted separately. Fast Discrete Orthonormal Stockwell Transform is exploited to extract the local features. Global feature is attained by escalating the size of Fast Discrete Orthonormal Stockwell Transform to infinity. Two features are fused to increase the recognition accuracy. A matching distance is calculated for both the features individually. Then two distances are merged mutually to acquire the final matching distance. The proposed scheme gives the better performance in terms of equal error rate and correct recognition rate.
Keywords: Hamming distance, Instantaneous phase, Region of Interest, Recognition accuracy.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27491557 A Study of the Variability of Very Low Resolution Characters and the Feasibility of Their Discrimination Using Geometrical Features
Authors: Farshideh Einsele, Rolf Ingold
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Current OCR technology does not allow to accurately recognizing small text images, such as those found in web images. Our goal is to investigate new approaches to recognize very low resolution text images containing antialiased character shapes. This paper presents a preliminary study on the variability of such characters and the feasibility to discriminate them by using geometrical features. In a first stage we analyze the distribution of these features. In a second stage we present a study on the discriminative power for recognizing isolated characters, using various rendering methods and font properties. Finally we present interesting results of our evaluation tests leading to our conclusion and future focus.Keywords: World Wide Web, document analysis, pattern recognition, Optical Character Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13621556 Porul: Option Generation and Selection and Scoring Algorithms for a Tamil Flash Card Game
Authors: Anitha Narasimhan, Aarthy Anandan, Madhan Karky, C. N. Subalalitha
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Games can be the excellent tools for teaching a language. There are few e-learning games in Indian languages like word scrabble, cross word, quiz games etc., which were developed mainly for educational purposes. This paper proposes a Tamil word game called, “Porul”, which focuses on education as well as on players’ thinking and decision-making skills. Porul is a multiple choice based quiz game, in which the players attempt to answer questions correctly from the given multiple options that are generated using a unique algorithm called the Option Selection algorithm which explores the semantics of the question in various dimensions namely, synonym, rhyme and Universal Networking Language semantic category. This kind of semantic exploration of the question not only increases the complexity of the game but also makes it more interesting. The paper also proposes a Scoring Algorithm which allots a score based on the popularity score of the question word. The proposed game has been tested using 20,000 Tamil words.Keywords: Porul game, Tamil word game, option selection, flash card, scoring, algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11501555 Image Similarity: A Genetic Algorithm Based Approach
Authors: R. C. Joshi, Shashikala Tapaswi
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The paper proposes an approach using genetic algorithm for computing the region based image similarity. The image is denoted using a set of segmented regions reflecting color and texture properties of an image. An image is associated with a family of image features corresponding to the regions. The resemblance of two images is then defined as the overall similarity between two families of image features, and quantified by a similarity measure, which integrates properties of all the regions in the images. A genetic algorithm is applied to decide the most plausible matching. The performance of the proposed method is illustrated using examples from an image database of general-purpose images, and is shown to produce good results.Keywords: Image Features, color descriptor, segmented classes, texture descriptors, genetic algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23131554 Learning to Recognize Faces by Local Feature Design and Selection
Authors: Yanwei Pang, Lei Zhang, Zhengkai Liu
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Studies in neuroscience suggest that both global and local feature information are crucial for perception and recognition of faces. It is widely believed that local feature is less sensitive to variations caused by illumination, expression and illumination. In this paper, we target at designing and learning local features for face recognition. We designed three types of local features. They are semi-global feature, local patch feature and tangent shape feature. The designing of semi-global feature aims at taking advantage of global-like feature and meanwhile avoiding suppressing AdaBoost algorithm in boosting weak classifies established from small local patches. The designing of local patch feature targets at automatically selecting discriminative features, and is thus different with traditional ways, in which local patches are usually selected manually to cover the salient facial components. Also, shape feature is considered in this paper for frontal view face recognition. These features are selected and combined under the framework of boosting algorithm and cascade structure. The experimental results demonstrate that the proposed approach outperforms the standard eigenface method and Bayesian method. Moreover, the selected local features and observations in the experiments are enlightening to researches in local feature design in face recognition.Keywords: Face recognition, local feature, AdaBoost, subspace analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15841553 Active Segment Selection Method in EEG Classification Using Fractal Features
Authors: Samira Vafaye Eslahi
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BCI (Brain Computer Interface) is a communication machine that translates brain massages to computer commands. These machines with the help of computer programs can recognize the tasks that are imagined. Feature extraction is an important stage of the process in EEG classification that can effect in accuracy and the computation time of processing the signals. In this study we process the signal in three steps of active segment selection, fractal feature extraction, and classification. One of the great challenges in BCI applications is to improve classification accuracy and computation time together. In this paper, we have used student’s 2D sample t-statistics on continuous wavelet transforms for active segment selection to reduce the computation time. In the next level, the features are extracted from some famous fractal dimension estimation of the signal. These fractal features are Katz and Higuchi. In the classification stage we used ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier, FKNN (Fuzzy K-Nearest Neighbors), LDA (Linear Discriminate Analysis), and SVM (Support Vector Machines). We resulted that active segment selection method would reduce the computation time and Fractal dimension features with ANFIS analysis on selected active segments is the best among investigated methods in EEG classification.
Keywords: EEG, Student’s t- statistics, BCI, Fractal Features, ANFIS, FKNN.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21081552 Speaker Identification using Neural Networks
Authors: R.V Pawar, P.P.Kajave, S.N.Mali
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The speech signal conveys information about the identity of the speaker. The area of speaker identification is concerned with extracting the identity of the person speaking the utterance. As speech interaction with computers becomes more pervasive in activities such as the telephone, financial transactions and information retrieval from speech databases, the utility of automatically identifying a speaker is based solely on vocal characteristic. This paper emphasizes on text dependent speaker identification, which deals with detecting a particular speaker from a known population. The system prompts the user to provide speech utterance. System identifies the user by comparing the codebook of speech utterance with those of the stored in the database and lists, which contain the most likely speakers, could have given that speech utterance. The speech signal is recorded for N speakers further the features are extracted. Feature extraction is done by means of LPC coefficients, calculating AMDF, and DFT. The neural network is trained by applying these features as input parameters. The features are stored in templates for further comparison. The features for the speaker who has to be identified are extracted and compared with the stored templates using Back Propogation Algorithm. Here, the trained network corresponds to the output; the input is the extracted features of the speaker to be identified. The network does the weight adjustment and the best match is found to identify the speaker. The number of epochs required to get the target decides the network performance.Keywords: Average Mean Distance function, Backpropogation, Linear Predictive Coding, MultilayeredPerceptron,
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18851551 Data Security in a DApp Twitter Alike on Web 3.0 With Blockchain Based Technology
Authors: Vishal Awasthi, Tanya Soni, Vigya Awasthi, Swati Singh, Shivali Verma
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There is a growing demand for a network that grants a high level of data security and confidentiality. For this reason, the semantic web was introduced, which allows data to be shared and reused across applications while safeguarding users privacy and user’s will grab back control of their data. The earlier Web 1.0 and Web 2.0 versions were built on client-server architecture, in which there was the risk of data theft and unconsented sale of user data. A decentralized version, Known as Web 3.0, that is mostly built on blockchain technology was interjected to resolve these issues. The recent research focuses on blockchain technology, deals with privacy, security, transparency, and innovation of decentralized applications (DApps), e.g. a Twitter Clone, Whatsapp clone. In this paper the Twitter Alike built on the Ethereum blockchain will replace traditional techniques with improved latency, throughput, and data ownership. The central principle of this DApp is smart contract implemented using Solidity which is an object- oriented and highlevel language. Consequently, this will provide a better Quality Services, high data security, and integrity for both present and future internet technologies.
Keywords: Blockchain, DApps, Ethereum, Semantic Web, Smart Contract, Solidity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2871550 Morpho-Anatomical and Ecological Studies on Endemic of Fritillaria oranensis Pomel. from the Mounts of Tessala (Western Algeria)
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Fritillaria oranensis (Liliaceae) was described in 1874 by pomel from Algeria. Plant samples have been collected from the mount of Tessala (Sidi-Bel-Abbes). The morphological features of various organs of the plant are described in detail. In the morphological part of the study, features of various organs of the plants such as stem and leaf were determined and illustrated. Ecological studies provide information about the physical and chemical structure of soil types in Tessala Mountain. The aim of this original investigation is to put forth ecological and anatomical features of these species for the first time, but at the same time given detailed account of the morphological characteristics of the stem and leaf of Fritillaria oranensis.
Keywords: Anatomy, ecology, Liliaceae, morphology, Fritillaria oranensis Pomel.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18151549 Improving Worm Detection with Artificial Neural Networks through Feature Selection and Temporal Analysis Techniques
Authors: Dima Stopel, Zvi Boger, Robert Moskovitch, Yuval Shahar, Yuval Elovici
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Computer worm detection is commonly performed by antivirus software tools that rely on prior explicit knowledge of the worm-s code (detection based on code signatures). We present an approach for detection of the presence of computer worms based on Artificial Neural Networks (ANN) using the computer's behavioral measures. Identification of significant features, which describe the activity of a worm within a host, is commonly acquired from security experts. We suggest acquiring these features by applying feature selection methods. We compare three different feature selection techniques for the dimensionality reduction and identification of the most prominent features to capture efficiently the computer behavior in the context of worm activity. Additionally, we explore three different temporal representation techniques for the most prominent features. In order to evaluate the different techniques, several computers were infected with five different worms and 323 different features of the infected computers were measured. We evaluated each technique by preprocessing the dataset according to each one and training the ANN model with the preprocessed data. We then evaluated the ability of the model to detect the presence of a new computer worm, in particular, during heavy user activity on the infected computers.Keywords: Artificial Neural Networks, Feature Selection, Temporal Analysis, Worm Detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17151548 Support Vector Machine Prediction Model of Early-stage Lung Cancer Based on Curvelet Transform to Extract Texture Features of CT Image
Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei
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Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27481547 Combined Feature Based Hyperspectral Image Classification Technique Using Support Vector Machines
Authors: Mrs.K.Kavitha, S.Arivazhagan
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A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.
Keywords: Multi-class, Run Length features, PCA, ICA, classification and Support Vector Machines.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15111546 A Proposed Hybrid Approach for Feature Selection in Text Document Categorization
Authors: M. F. Zaiyadi, B. Baharudin
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Text document categorization involves large amount of data or features. The high dimensionality of features is a troublesome and can affect the performance of the classification. Therefore, feature selection is strongly considered as one of the crucial part in text document categorization. Selecting the best features to represent documents can reduce the dimensionality of feature space hence increase the performance. There were many approaches has been implemented by various researchers to overcome this problem. This paper proposed a novel hybrid approach for feature selection in text document categorization based on Ant Colony Optimization (ACO) and Information Gain (IG). We also presented state-of-the-art algorithms by several other researchers.Keywords: Ant colony optimization, feature selection, information gain, text categorization, text representation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20541545 Features of Soil Formation in the North of Western Siberia in Cryogenic Conditions
Authors: Tatiana V. Raudina, Sergey P. Kulizhskiy
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A large part of Russia is located in permafrost areas. These areas are widely used because there are concentrated valuable natural resources. Therefore to explore of cryosols it is important due to the significant increase of anthropogenic stress as well as the problem of global climate change. In the north of Western Siberia permafrost phenomena is widespread. Permafrost as a factor of soil formation and cryogenesis as a process have a great impact on the soil formation of these areas. Based on the research results of permafrost-affected soils tundra landscapes formed in the central part of the Tazovskiy Peninsula in cryogenic conditions, data were obtained which characterize the morphological features of soils. The specificity of soil cover distribution and manifestation of soil-forming processes within the study area are noted. Permafrost features such as frost cracking, cryoturbation, thixotropy, movement of humus are formed. The formation of these features is increased with the development of the territory. As a consequence, there is a change in the components of the environment and the destruction of the soil cover.
Keywords: Gleyed and nongleyed soils, permafrost, soil cryogenesis (pedocryogenesis), soil-forming macroprocesses.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20521544 Application of Genetic Algorithms to Feature Subset Selection in a Farsi OCR
Authors: M. Soryani, N. Rafat
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Dealing with hundreds of features in character recognition systems is not unusual. This large number of features leads to the increase of computational workload of recognition process. There have been many methods which try to remove unnecessary or redundant features and reduce feature dimensionality. Besides because of the characteristics of Farsi scripts, it-s not possible to apply other languages algorithms to Farsi directly. In this paper some methods for feature subset selection using genetic algorithms are applied on a Farsi optical character recognition (OCR) system. Experimental results show that application of genetic algorithms (GA) to feature subset selection in a Farsi OCR results in lower computational complexity and enhanced recognition rate.Keywords: Feature Subset Selection, Genetic Algorithms, Optical Character Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19671543 Intensifier as Changed from the Impolite Word in Thai
Authors: Methawee Yuttapongtada
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Intensifier is the linguistic term and device that is generally found in different languages in order to enhance and give additional quantity, quality or emotion to the words of each language. In fact, each language in the world has both of the similar and dissimilar intensifying device. More specially, the wide variety of intensifying device is used for Thai language and one of those is usage of the impolite word or the word that used to mean something negative as intensifier. The data collection in this study was done throughout the spoken language style by collecting from intensifiers regarded as impolite words because these words as employed in the other contexts will be held as the rude, swear words or the words with negative meaning. Then, backward study to the past was done in order to consider the historical change. Explanation of the original meaning and the contexts of words use from the past till the present time were done by use of both textual documents and dictionaries available in different periods. It was found that regarding the semantics and pragmatic aspects, subjectification also is the significant motivation that changed the impolite words to intensifiers. At last, it can explain pathway of the semantic change of these very words undoubtedly. Moreover, it is found that use tendency in the impolite word or the word that used to mean something negative will more be increased and this phenomenon is commonly found in many languages in the world and results of this research may support to the belief that human language in the world is universal and the same still reflected that human has the fundamental thought as the same to each other basically.
Keywords: Impolite word, intensifier, Thai, semantic change.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13011542 Unequal Error Protection of Facial Features for Personal ID Images Coding
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This paper presents an approach for an unequal error protection of facial features of personal ID images coding. We consider unequal error protection (UEP) strategies for the efficient progressive transmission of embedded image codes over noisy channels. This new method is based on the progressive image compression embedded zerotree wavelet (EZW) algorithm and UEP technique with defined region of interest (ROI). In this case is ROI equal facial features within personal ID image. ROI technique is important in applications with different parts of importance. In ROI coding, a chosen ROI is encoded with higher quality than the background (BG). Unequal error protection of image is provided by different coding techniques and encoding LL band separately. In our proposed method, image is divided into two parts (ROI, BG) that consist of more important bytes (MIB) and less important bytes (LIB). The proposed unequal error protection of image transmission has shown to be more appropriate to low bit rate applications, producing better quality output for ROI of the compresses image. The experimental results verify effectiveness of the design. The results of our method demonstrate the comparison of the UEP of image transmission with defined ROI with facial features and the equal error protection (EEP) over additive white gaussian noise (AWGN) channel.Keywords: Embedded zerotree wavelet (EZW), equal error protection (EEP), facial features, personal ID images, region of interest (ROI), unequal error protection (UEP)
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14831541 Face Recognition Using Discrete Orthogonal Hahn Moments
Authors: Fatima Akhmedova, Simon Liao
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One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, nonredundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.Keywords: Face Recognition, Hahn moments, Recognition-by-parts, Time-lapse.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17661540 A Neural Approach for Color-Textured Images Segmentation
Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui
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In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.Keywords: Segmentation, color-texture, neural networks, fractal, watershed.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13671539 Feature-Based Machining using Macro
Authors: M. Razak, A. Jusoh, A. Zakaria
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This paper presents an on-going research work on the implementation of feature-based machining via macro programming. Repetitive machining features such as holes, slots, pockets etc can readily be encapsulated in macros. Each macro consists of methods on how to machine the shape as defined by the feature. The macro programming technique comprises of a main program and subprograms. The main program allows user to select several subprograms that contain features and define their important parameters. With macros, complex machining routines can be implemented easily and no post processor is required. A case study on machining of a part that comprised of planar face, hole and pocket features using the macro programming technique was carried out. It is envisaged that the macro programming technique can be extended to other feature-based machining fields such as the newly developed STEP-NC domain.Keywords: Feature-based machining, CNC, Macro, STEP-NC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26731538 Influence of Social-Psychological Training on Selected Features of University Students
Authors: Anežka Hamranová, Blandína Šramová, Katarína Fichnová
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We presented results of research aimed on findings influence of social - psychological training (realized with students of Constantine the Philosopher University- future teachers within their undergraduate preparation) on the choice of intrapersonal and interpersonal features. After social- psychological training using Interpersonal Check List (ICL) we found out shift of behavior to more adaptive forms in categories, which are characterized by extroversive friendly behavior, willingness to cooperation, conformity regard to social situation, responsible and regardful behavior. Using State-Trait Anxiety Inventory (STAI) we found out the cut down of state anxiety and of trait anxiety. The report was processed within grants KEGA 3/5269/07 and VEGA 1/3675/06.Keywords: Intrapersonal and interpersonal features, social -psychological training, social competences.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15631537 Detecting HCC Tumor in Three Phasic CT Liver Images with Optimization of Neural Network
Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza
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The aim of this work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. With our research and based on a feature selection in different phases, we are trying to design a neural network system with an optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each region of interest (ROI), 6 distinct sets of texture features are extracted such as: first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. When analyzing more phases, we show that the injection of liquid cause changes to the high relevant features in each region. Our results demonstrate that for detecting HCC tumor phase 3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between pathology and healthy classes, according to our method, relates to first order histogram parameters with accuracy of 85% in phase 1, 95% in phase 2, and 95% in phase 3.
Keywords: Feature selection, Multi-phasic liver images, Neural network, Texture analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25271536 Text-independent Speaker Identification Based on MAP Channel Compensation and Pitch-dependent Features
Authors: Jiqing Han, Rongchun Gao
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One major source of performance decline in speaker recognition system is channel mismatch between training and testing. This paper focuses on improving channel robustness of speaker recognition system in two aspects of channel compensation technique and channel robust features. The system is text-independent speaker identification system based on two-stage recognition. In the aspect of channel compensation technique, this paper applies MAP (Maximum A Posterior Probability) channel compensation technique, which was used in speech recognition, to speaker recognition system. In the aspect of channel robust features, this paper introduces pitch-dependent features and pitch-dependent speaker model for the second stage recognition. Based on the first stage recognition to testing speech using GMM (Gaussian Mixture Model), the system uses GMM scores to decide if it needs to be recognized again. If it needs to, the system selects a few speakers from all of the speakers who participate in the first stage recognition for the second stage recognition. For each selected speaker, the system obtains 3 pitch-dependent results from his pitch-dependent speaker model, and then uses ANN (Artificial Neural Network) to unite the 3 pitch-dependent results and 1 GMM score for getting a fused result. The system makes the second stage recognition based on these fused results. The experiments show that the correct rate of two-stage recognition system based on MAP channel compensation technique and pitch-dependent features is 41.7% better than the baseline system for closed-set test.Keywords: Channel Compensation, Channel Robustness, MAP, Speaker Identification
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15361535 A Robust Visual Tracking Algorithm with Low-Rank Region Covariance
Authors: Songtao Wu, Yuesheng Zhu, Ziqiang Sun
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Region covariance (RC) descriptor is an effective and efficient feature for visual tracking. Current RC-based tracking algorithms use the whole RC matrix to track the target in video directly. However, there exist some issues for these whole RCbased algorithms. If some features are contaminated, the whole RC will become unreliable, which results in lost object-tracking. In addition, if some features are very discriminative to the background, other features are still processed and thus reduce the efficiency. In this paper a new robust tracking method is proposed, in which the whole RC matrix is decomposed into several low rank matrices. Those matrices are dynamically chosen and processed so as to achieve a good tradeoff between discriminability and complexity. Experimental results have shown that our method is more robust to complex environment changes, especially either when occlusion happens or when the background is similar to the target compared to other RC-based methods.Keywords: Visual tracking, region covariance descriptor, lowrankregion covariance
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15721534 A Domain Specific Modeling Language Semantic Model for Artefact Orientation
Authors: Bunakiye R. Japheth, Ogude U. Cyril
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Since the process of transforming user requirements to modeling constructs are not very well supported by domain-specific frameworks, it became necessary to integrate domain requirements with the specific architectures to achieve an integrated customizable solutions space via artifact orientation. Domain-specific modeling language specifications of model-driven engineering technologies focus more on requirements within a particular domain, which can be tailored to aid the domain expert in expressing domain concepts effectively. Modeling processes through domain-specific language formalisms are highly volatile due to dependencies on domain concepts or used process models. A capable solution is given by artifact orientation that stresses on the results rather than expressing a strict dependence on complicated platforms for model creation and development. Based on this premise, domain-specific methods for producing artifacts without having to take into account the complexity and variability of platforms for model definitions can be integrated to support customizable development. In this paper, we discuss methods for the integration capabilities and necessities within a common structure and semantics that contribute a metamodel for artifact-orientation, which leads to a reusable software layer with concrete syntax capable of determining design intents from domain expert. These concepts forming the language formalism are established from models explained within the oil and gas pipelines industry.
Keywords: Control process, metrics of engineering, structured abstraction, semantic model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7281533 Fingerprint Identification using Discretization Technique
Authors: W. Y. Leng, S. M. Shamsuddin
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Fingerprint based identification system; one of a well known biometric system in the area of pattern recognition and has always been under study through its important role in forensic science that could help government criminal justice community. In this paper, we proposed an identification framework of individuals by means of fingerprint. Different from the most conventional fingerprint identification frameworks the extracted Geometrical element features (GEFs) will go through a Discretization process. The intention of Discretization in this study is to attain individual unique features that could reflect the individual varianceness in order to discriminate one person from another. Previously, Discretization has been shown a particularly efficient identification on English handwriting with accuracy of 99.9% and on discrimination of twins- handwriting with accuracy of 98%. Due to its high discriminative power, this method is adopted into this framework as an independent based method to seek for the accuracy of fingerprint identification. Finally the experimental result shows that the accuracy rate of identification of the proposed system using Discretization is 100% for FVC2000, 93% for FVC2002 and 89.7% for FVC2004 which is much better than the conventional or the existing fingerprint identification system (72% for FVC2000, 26% for FVC2002 and 32.8% for FVC2004). The result indicates that Discretization approach manages to boost up the classification effectively, and therefore prove to be suitable for other biometric features besides handwriting and fingerprint.Keywords: Discretization, fingerprint identification, geometrical features, pattern recognition
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23461532 Grouping and Indexing Color Features for Efficient Image Retrieval
Authors: M. V. Sudhamani, C. R. Venugopal
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Content-based Image Retrieval (CBIR) aims at searching image databases for specific images that are similar to a given query image based on matching of features derived from the image content. This paper focuses on a low-dimensional color based indexing technique for achieving efficient and effective retrieval performance. In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique. Then the cluster (region) mode is used as representative of the image in 3-D color space. The feature descriptor consists of the representative color of a region and is indexed using a spatial indexing method that uses *R -tree thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. Alternatively, the images in the database are clustered based on region feature similarity using Euclidian distance. Only representative (centroids) features of these clusters are indexed using *R -tree thus improving the efficiency. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The results of these methods are compared. A JAVA based query engine supporting query-by- example is built to retrieve images by color.
Keywords: Content-based, indexing, cluster, region.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18031531 Feature Selection for Web Page Classification Using Swarm Optimization
Authors: B. Leela Devi, A. Sankar
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The web’s increased popularity has included a huge amount of information, due to which automated web page classification systems are essential to improve search engines’ performance. Web pages have many features like HTML or XML tags, hyperlinks, URLs and text contents which can be considered during an automated classification process. It is known that Webpage classification is enhanced by hyperlinks as it reflects Web page linkages. The aim of this study is to reduce the number of features to be used to improve the accuracy of the classification of web pages. In this paper, a novel feature selection method using an improved Particle Swarm Optimization (PSO) using principle of evolution is proposed. The extracted features were tested on the WebKB dataset using a parallel Neural Network to reduce the computational cost.
Keywords: Web page classification, WebKB Dataset, Term Frequency-Inverse Document Frequency (TF-IDF), Particle Swarm Optimization (PSO).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32511530 Investigation of Some Methodologies in Providing Erosion Maps of Surface, Rill and Gully and Erosion Features
Authors: A. Mohammadi Torkashvand, N. Haghighat
Abstract:
Some methodologies were compared in providing erosion maps of surface, rill and gully and erosion features, in research which took place in the Varamin sub-basin, north-east Tehran, Iran. A photomorphic unit map was produced from processed satellite images, and four other maps were prepared by the integration of different data layers, including slope, plant cover, geology, land use, rocks erodibility and land units. Comparison of ground truth maps of erosion types and working unit maps indicated that the integration of land use, land units and rocks erodibility layers with satellite image photomorphic units maps provide the best methods in producing erosion types maps.Keywords: Erosion Features, Geographic Information System, Remote Sensing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17691529 Speaker Identification Using Admissible Wavelet Packet Based Decomposition
Authors: Mangesh S. Deshpande, Raghunath S. Holambe
Abstract:
Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable for speaker features that are located in high frequency regions. The speaker individual information, which is non-uniformly distributed in the high frequencies, is equally important for speaker recognition. Based on this fact we proposed an admissible wavelet packet based filter structure for speaker identification. Multiresolution capabilities of wavelet packet transform are used to derive the new features. The proposed scheme differs from previous wavelet based works, mainly in designing the filter structure. Unlike others, the proposed filter structure does not follow Mel scale. The closed-set speaker identification experiments performed on the TIMIT database shows improved identification performance compared to other commonly used Mel scale based filter structures using wavelets.Keywords: Speaker identification, Wavelet transform, Feature extraction, MFCC, GMM.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1974