Search results for: smartphone applications; photo mosaic; similarity search; data mining; large-scale image databases.

Authors: Vitomir Struc, Nikola Pavesic

Abstract:

Over the past decades, automatic face recognition has become a highly active research area, mainly due to the countless application possibilities in both the private as well as the public sector. Numerous algorithms have been proposed in the literature to cope with the problem of face recognition, nevertheless, a group of methods commonly referred to as appearance based have emerged as the dominant solution to the face recognition problem. Many comparative studies concerned with the performance of appearance based methods have already been presented in the literature, not rarely with inconclusive and often with contradictory results. No consent has been reached within the scientific community regarding the relative ranking of the efficiency of appearance based methods for the face recognition task, let alone regarding their susceptibility to appearance changes induced by various environmental factors. To tackle these open issues, this paper assess the performance of the three dominant appearance based methods: principal component analysis, linear discriminant analysis and independent component analysis, and compares them on equal footing (i.e., with the same preprocessing procedure, with optimized parameters for the best possible performance, etc.) in face verification experiments on the publicly available XM2VTS database. In addition to the comparative analysis on the XM2VTS database, ten degraded versions of the database are also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in "real-life" operating conditions. Our experimental results suggest that linear discriminant analysis ensures the most consistent verification rates across the tested databases.

Keywords: Biometrics, face recognition, appearance based methods, image degradations, the XM2VTS database.

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Authors: N. D. Osman, M. S. Salikin, M. I. Saripan

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CT assessment of postoperative spine is challenging in the presence of metal streak artifacts that could deteriorate the quality of CT images. In this paper, we studied the influence of different acquisition parameters on the magnitude of metal streaking. A water-bath phantom was constructed with metal insertion similar with postoperative spine assessment. The phantom was scanned with different acquisition settings and acquired data were reconstructed using various reconstruction settings. Standardized ROIs were defined within streaking region for image analysis. The result shows increased kVp and mAs enhanced SNR values by reducing image noise. Sharper kernel enhanced image quality compared to smooth kernel, but produced more noise in the images with higher CT fluctuation. The noise between both kernels were significantly different (P <0.05) with increment of noise in the bone kernel images (mean difference = 54.78). The technical settings should be selected appropriately to attain the acceptable image quality with the best diagnostic value.

Keywords: Computed tomography, metal streak, noise, CT fluctuation.

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Authors: Sujata Upgupta, Prasoon Kumar Singh

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The expansion of large scale coal mining into forest areas is a potential hazard for the local biodiversity and wildlife. The objective of this study is to provide a picture of the threat that coal mining poses to the forests of the East Bokaro landscape. The vulnerable forest areas at risk have been assessed and the priority areas for conservation have been presented. The forested areas at risk in the current scenario have been assessed and compared with the past conditions using classification and buffer based overlay approach. Forest vulnerability has been assessed using an analytical framework based on systematic indicators and composite vulnerability index values. The results indicate that more than 4 km² of forests have been lost from 1973 to 2016. Large patches of forests have been diverted for coal mining projects. Forests in the northern part of the coal field within 1-3 km radius around the coal mines are at immediate risk. The original contiguous forests have been converted into fragmented and degraded forest patches. Most of the collieries are located within or very close to the forests thus threatening the biodiversity and hydrology of the surrounding regions. Based on the vulnerability values estimated, it was concluded that more than 90% of the forested grids in East Bokaro are highly vulnerable to mining. The forests in the sub-districts of Bermo and Chandrapura have been identified as the most vulnerable to coal mining activities. This case study would add to the capacity of the forest managers and mine managers to address the risk and vulnerability of forests at a small landscape level in order to achieve sustainable development.

Keywords: Coal mining, forest, indicators, vulnerability.

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Authors: Farhad Kolahan, M. Hossein Abolbashari, Samaeddin Mohitzadeh

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Several methods are available for weight and shape optimization of structures, among which Evolutionary Structural Optimization (ESO) is one of the most widely used methods. In ESO, however, the optimization criterion is completely case-dependent. Moreover, only the improving solutions are accepted during the search. In this paper a Simulated Annealing (SA) algorithm is used for structural optimization problem. This algorithm differs from other random search methods by accepting non-improving solutions. The implementation of SA algorithm is done through reducing the number of finite element analyses (function evaluations). Computational results show that SA can efficiently and effectively solve such optimization problems within short search time.

Keywords: Simulated annealing, Structural optimization, Compliance, C.V. product.

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Authors: Masaaki Saito, Qin Tang, Hiroyuki Umemuro

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The purpose of this paper is to propose a text mining approach to evaluate companies- practices on affective management. Affective management argues that it is critical to take stakeholders- affects into consideration during decision-making process, along with the traditional numerical and rational indices. CSR reports published by companies were collected as source information. Indices were proposed based on the frequency and collocation of words relevant to affective management concept using text mining approach to analyze the text information of CSR reports. In addition, the relationships between the results obtained using proposed indices and traditional indicators of business performance were investigated using correlation analysis. Those correlations were also compared between manufacturing and non-manufacturing companies. The results of this study revealed the possibility to evaluate affective management practices of companies based on publicly available text documents.

Keywords: Affective management, Affect, Stakeholder, Text mining.

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Authors: Mehran Yazdi, Kazem Gheysari

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In this paper, we propose an approach for the classification of fingerprint databases. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices. So, we first extract the features based on certain characteristics of the cooccurrence matrix and then we use these features to train a neural network for classifying fingerprints into four common classes. The obtained results compared with the existing approaches demonstrate the superior performance of our proposed approach.

Keywords: Biometrics, fingerprint classification, gray level cooccurrence matrix, regular texture representation.

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Authors: Jehad Al Dallal

Abstract:

Class cohesion is an important object-oriented software quality attributes, and it refers to the degree of relatedness of class attributes and methods. Several class cohesion measures are proposed in the literature, and the impact of considering the special methods (i.e., constructors, destructors, and access and delegation methods) in cohesion calculation is not thoroughly theoretically studied for most of them. In this paper, we address this issue for three popular similarity-based class cohesion measures. For each of the considered measures we theoretically study the impact of including or excluding special methods on the values that are obtained by applying the measure. This study is based on analyzing the definitions and formulas that are proposed for the measures. The results show that including/excluding special methods has a considerable effect on the obtained cohesion values and that this effect varies from one measure to another. The study shows the importance of considering the types of methods that have to be accounted for when proposing a similarity-based cohesion measure.

Keywords: Object-oriented class, software quality, class cohesion measure, class cohesion, special methods.

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Authors: Yao-Hong Tsai

Abstract:

This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.

Keywords: Image fusion, iris recognition, local binary pattern, wavelet.

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Authors: Pushkar Umaranikar, Chris Pollett

Abstract:

An online advertisement system and its implementation for the Yioop open source search engine are presented. This system supports both selling advertisements and displaying them within search results. The selling of advertisements is done using a system to auction off daily impressions for keyword searches. This is an open, ascending price auction system in which all accepted bids will receive a fraction of the auctioned day’s impressions. New bids in our system are required to be at least one half of the sum of all previous bids ensuring the number of accepted bids is logarithmic in the total ad spend on a keyword for a day. The mechanics of creating an advertisement, attaching keywords to it, and adding it to an advertisement inventory are described. The algorithm used to go from accepted bids for a keyword to which ads are displayed at search time is also presented. We discuss properties of our system and compare it to existing auction systems and systems for selling online advertisements.

Keywords: Online markets, online ad system, online auctions, search engines.

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Authors: Jin Myoung Kim, Tae Ho Cho

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Wireless Sensor networks have a wide spectrum of civil and military applications that call for secure communication such as the terrorist tracking, target surveillance in hostile environments. For the secure communication in these application areas, we propose a method for generating a hierarchical key structure for the efficient group key management. In this paper, we apply A* algorithm in generating a hierarchical key structure by considering the history data of the ratio of addition and eviction of sensor nodes in a location where sensor nodes are deployed. Thus generated key tree structure provides an efficient way of managing the group key in terms of energy consumption when addition and eviction event occurs. A* algorithm tries to minimize the number of messages needed for group key management by the history data. The experimentation with the tree shows efficiency of the proposed method.

Keywords: Heuristic search, key management, security, sensor network.

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Authors: Omer K. Jasim, Safia Abbas, El-Sayed M. El-Horbaty, Abdel-Badeeh M. Salem

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Cloud computing technology is very useful in present day to day life, it uses the internet and the central remote servers to provide and maintain data as well as applications. Such applications in turn can be used by the end users via the cloud communications without any installation. Moreover, the end users’ data files can be accessed and manipulated from any other computer using the internet services. Despite the flexibility of data and application accessing and usage that cloud computing environments provide, there are many questions still coming up on how to gain a trusted environment that protect data and applications in clouds from hackers and intruders. This paper surveys the “keys generation and management” mechanism and encryption/decryption algorithms used in cloud computing environments, we proposed new security architecture for cloud computing environment that considers the various security gaps as much as possible. A new cryptographic environment that implements quantum mechanics in order to gain more trusted with less computation cloud communications is given.

Keywords: Cloud Computing, Cloud Encryption Model, Quantum Key Distribution.

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Authors: Renáta Iváncsy, István Vajk

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The various types of frequent pattern discovery problem, namely, the frequent itemset, sequence and graph mining problems are solved in different ways which are, however, in certain aspects similar. The main approach of discovering such patterns can be classified into two main classes, namely, in the class of the levelwise methods and in that of the database projection-based methods. The level-wise algorithms use in general clever indexing structures for discovering the patterns. In this paper a new approach is proposed for discovering frequent sequences and tree-like patterns efficiently that is based on the level-wise issue. Because the level-wise algorithms spend a lot of time for the subpattern testing problem, the new approach introduces the idea of using automaton theory to solve this problem.

Keywords: Frequent pattern discovery, graph mining, pushdownautomaton, sequence mining, state machine, tree mining.

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Authors: C. R. Balamurugan, P. Vijayakumar, P. Kiruba, S. Arun Kanna, E. R. Hariprasath, C. Anu Priya

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This paper studies in details about the need of quadcopter in various fields especially in the place of remote area where the road transportation facility is very less. It is used to monitor and collect data in a specific region. The movement of this quadcopter is controlled by the Raspberry Pi. FPV camera is used for capturing the image and will transmit the image to the receiver which can be monitored using an android smart phone. This is mainly used for surveillance purpose and hidden activities can be captured.

Keywords: FPV camera, A2212 brushless direct current motor, Raspberry Pi, lithium polymer battery.

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Authors: Marrick Neri, Esmeraldo Ronnie Rey Zara

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In this paper, we apply a semismooth active set method to image inpainting. The method exploits primal and dual features of a proposed regularized total variation model, following after the technique presented in [4]. Numerical results show that the method is fast and efficient in inpainting sufficiently thin domains.

Keywords: Active set method, image inpainting, total variation model.

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Authors: Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh K. Pandey

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The edges of low contrast images are not clearly distinguishable to human eye. It is difficult to find the edges and boundaries in it. The present work encompasses a new approach for low contrast images. The Chebyshev polynomial based fractional order filter has been used for filtering operation on an image. The preprocessing has been performed by this filter on the input image. Laplacian of Gaussian method has been applied on preprocessed image for edge detection. The algorithm has been tested on two test images.

Keywords: Chebyshev polynomials, Fractional order differentiator, Laplacian of Gaussian (LoG) method, Low contrast image.

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Authors: Jameela Ali Alkrimi, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithms tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital - Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-Nearest Neighbors, Neural Network, Radial Basis Function, Red blood cells, Support vector machine.

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Authors: Nhu Van NGUYEN, Jean-Marc OGIER, Salvatore TABBONE, Alain BOUCHER

Abstract:

The state-of-the-art Bag of Words model in Content- Based Image Retrieval has been used for years but the relevance feedback strategies for this model are not fully investigated. Inspired from text retrieval, the Bag of Words model has the ability to use the wealth of knowledge and practices available in text retrieval. We study and experiment the relevance feedback model in text retrieval for adapting it to image retrieval. The experiments show that the techniques from text retrieval give good results for image retrieval and that further improvements is possible.

Keywords: Relevance feedback, bag of words model, probabilistic model, vector space model, image retrieval

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Authors: Inder K. Purohit, M. Nazrul Islam, K. Vijayan Asari, Mohammad A. Karim

Abstract:

A new target detection technique is presented in this paper for the identification of small boats in coastal surveillance. The proposed technique employs an adaptive progressive thresholding (APT) scheme to first process the given input scene to separate any objects present in the scene from the background. The preprocessing step results in an image having only the foreground objects, such as boats, trees and other cluttered regions, and hence reduces the search region for the correlation step significantly. The processed image is then fed to the shifted phase-encoded fringe-adjusted joint transform correlator (SPFJTC) technique which produces single and delta-like correlation peak for a potential target present in the input scene. A post-processing step involves using a peak-to-clutter ratio (PCR) to determine whether the boat in the input scene is authorized or unauthorized. Simulation results are presented to show that the proposed technique can successfully determine the presence of an authorized boat and identify any intruding boat present in the given input scene.

Keywords: Adaptive progressive thresholding, fringe adjusted filters, image segmentation, joint transform correlation, synthetic discriminant function

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Authors: N. Manavizadeh , M. Rabbani , H. Sotudian , F. Jolai

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Mixed Model Production is the practice of assembling several distinct and different models of a product on the same assembly line without changeovers and then sequencing those models in a way that smoothes the demand for upstream components. In this paper, we consider an objective function which minimizes total stoppage time and total idle time and it is presented sequence dependent set up time. Many studies have been done on the mixed model assembly lines. But in this paper we specifically focused on reducing the idle times. This is possible through various help policies. For improving the solutions, some cases developed and about 40 tests problem was considered. We use scatter search for optimization and for showing the efficiency of our algorithm, experimental results shows behavior of method. Scatter search and help policies can produce high quality answers, so it has been used in this paper.

Keywords: mixed model assembly lines, Scatter search, help policies, idle time, Stoppage time

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Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara

Abstract:

Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.

Keywords: Ocular diseases, retinal fundus image, optic disc detection and segmentation, fully convolutional network, overlap measure.

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

Abstract:

In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent transportation systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning.

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Authors: Mukesh Kumar Shah, Tushar Gupta

Abstract:

An Upgraded Cuckoo Search Algorithm is proposed here to solve optimization problems based on the improvements made in the earlier versions of Cuckoo Search Algorithm. Short comings of the earlier versions like slow convergence, trap in local optima improved in the proposed version by random initialization of solution by suggesting an Improved Lambda Iteration Relaxation method, Random Gaussian Distribution Walk to improve local search and further proposing Greedy Selection to accelerate to optimized solution quickly and by “Study Nearby Strategy” to improve global search performance by avoiding trapping to local optima. It is further proposed to generate better solution by Crossover Operation. The proposed strategy used in algorithm shows superiority in terms of high convergence speed over several classical algorithms. Three standard algorithms were tested on a 6-generator standard test system and the results are presented which clearly demonstrate its superiority over other established algorithms. The algorithm is also capable of handling higher unit systems.

Keywords: Economic dispatch, Gaussian selection operator, prohibited operating zones, ramp rate limits, upgraded cuckoo search.

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Authors: Abdolreza Hatamlou, Yusef Farhang, Mohammad Reza Meybodi

Abstract:

Variable ordering heuristics are used in constraint satisfaction algorithms. Different characteristics of various variable ordering heuristics are complementary. Therefore we have tried to get the advantages of all heuristics to improve search algorithms performance for solving constraint satisfaction problems. This paper considers combinations based on products and quotients, and then a newer form of combination based on weighted sums of ratings from a set of base heuristics, some of which result in definite improvements in performance.

Keywords: Constraint Satisfaction Problems, Variable Ordering Heuristics, Combination, Search Algorithms

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Authors: Morten Stigaard Laursen, Rasmus Nyholm Jørgensen, Mads Dyrmann, Robert Poulsen

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For the past three years, the Danish project, RoboWeedSupport, has sought to bridge the gap between the potential herbicide savings using a decision support system and the required weed inspections. In order to automate the weed inspections it is desired to generate a map of the weed species present within the field, to generate the map images must be captured with samples covering the field. This paper investigates the economical cost of performing this data collection based on a camera system mounted on a all-terain vehicle (ATV) able to drive and collect data at up to 50 km/h while still maintaining a image quality sufficient for identifying newly emerged grass weeds. The economical estimates are based on approximately 100 hectares recorded at three different locations in Denmark. With an average image density of 99 images per hectare the ATV had an capacity of 28 ha per hour, which is estimated to cost 6.6 EUR/ha. Alternatively relying on a boom solution for an existing tracktor it was estimated that a cost of 2.4 EUR/ha is obtainable under equal conditions.

Keywords: Weed mapping, integrated weed management, weed recognition.

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Authors: Alessandro Bevilacqua, Alessandro Gherardi, Ludovico Carozza, Filippo Piccinini

Abstract:

The last years have seen an increasing use of image analysis techniques in the field of biomedical imaging, in particular in microscopic imaging. The basic step for most of the image analysis techniques relies on a background image free of objects of interest, whether they are cells or histological samples, to perform further analysis, such as segmentation or mosaicing. Commonly, this image consists of an empty field acquired in advance. However, many times achieving an empty field could not be feasible. Or else, this could be different from the background region of the sample really being studied, because of the interaction with the organic matter. At last, it could be expensive, for instance in case of live cell analyses. We propose a non parametric and general purpose approach where the background is built automatically stemming from a sequence of images containing even objects of interest. The amount of area, in each image, free of objects just affects the overall speed to obtain the background. Experiments with different kinds of microscopic images prove the effectiveness of our approach.

Keywords: Microscopy, flat field correction, background estimation, image segmentation.

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Authors: Felix David

Abstract:

The use of virtual reality (VR) technology is still in its infancy. This is especially true in a Japanese educational context with very little to no exposition of VR technology inside classrooms. Technology is growing and changing rapidly in America, but Japan seems to be lagging behind in integrating VR into its curriculum. The aim of this research was to expose 111 students from Hiroshima Jogakuin University (HJU) to seven classes that involved VR content and assess students’ attitudinal responses toward this new technology. The students are all female, and they are taking the “Kiso Eigo/基礎英語” or Foundation English course, which is mandatory for all first- and second-year students. Two surveys were given, one before the treatment and a second survey after the treatment, which in this case means the seven VR classes. These surveys first established that the technical environment could accommodate VR activities in terms of internet connection, VR headsets, and the quality of the smartphone’s screen. Based on the attitudinal responses gathered in this research, VR is perceived by students as “fun,” useful to “learn about the world,” as well as being useful to “learn about English.” This research validates VR as a worthy educational tool and it should therefore continue being an integral part of the mandatory English course curriculum at HJU.

Keywords: Virtual Reality, smartphone, English Learning, curriculum.

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Authors: R. M. Farouk

Abstract:

In this paper, we present an analytical analysis of the representation of images as the magnitudes of their transform with the discrete wavelets. Such a representation plays as a model for complex cells in the early stage of visual processing and of high technical usefulness for image understanding, because it makes the representation insensitive to small local shifts. We found that if the signals are band limited and of zero mean, then reconstruction from the magnitudes is unique up to the sign for almost all signals. We also present an iterative reconstruction algorithm which yields very good reconstruction up to the sign minor numerical errors in the very low frequencies.

Keywords: Wavelets, Image processing signal processing, Image reconstruction

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Authors: Morteza Moazami-Goudarzi, Mohammad H. Moradi, Ali Taheri

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In this paper we introduce an effective ECG compression algorithm based on two dimensional multiwavelet transform. Multiwavelets offer simultaneous orthogonality, symmetry and short support, which is not possible with scalar two-channel wavelet systems. These features are known to be important in signal processing. Thus multiwavelet offers the possibility of superior performance for image processing applications. The SPIHT algorithm has achieved notable success in still image coding. We suggested applying SPIHT algorithm to 2-D multiwavelet transform of2-D arranged ECG signals. Experiments on selected records of ECG from MIT-BIH arrhythmia database revealed that the proposed algorithm is significantly more efficient in comparison with previously proposed ECG compression schemes.

Keywords: ECG signal compression, multi-rateprocessing, 2-D Multiwavelet, Prefiltering.

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Authors: Irina Maria Artinescu, Costin Radu Boldea, Eduard-Ionut Matei

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The paper is a comparative study of two classical vari-ants of parallel projection methods for solving the convex feasibility problem with their equivalents that involve variable weights in the construction of the solutions. We used a graphical representation of these methods for inpainting a convex area of an image in order to investigate their effectiveness in image reconstruction applications. We also presented a numerical analysis of the convergence of these four algorithms in terms of the average number of steps and execution time, in classical CPU and, alternativaly, in parallel GPU implementation.

Keywords: convex feasibility problem, convergence analysis, ınpainting, parallel projection methods

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Authors: Hei-Chia Wang, Che-Tsung Yang

Abstract:

While the explosive increase in information published on the Web, researchers have to filter information when searching for conference related information. To make it easier for users to search related information, this paper uses Topic Maps and social information to implement ontology since ontology can provide the formalisms and knowledge structuring for comprehensive and transportable machine understanding that digital information requires. Besides enhancing information in Topic Maps, this paper proposes a method of constructing research Topic Maps considering social information. First, extract conference data from the web. Then extract conference topics and the relationships between them through the proposed method. Finally visualize it for users to search and browse. This paper uses ontology, containing abundant of knowledge hierarchy structure, to facilitate researchers getting useful search results. However, most previous ontology construction methods didn-t take “people" into account. So this paper also analyzes the social information which helps researchers find the possibilities of cooperation/combination as well as associations between research topics, and tries to offer better results.

Keywords: Ontology, topic maps, social information, co-authorship.

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