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Paper Count: 30836
Object Identification with Color, Texture, and Object-Correlation in CBIR System
Abstract:Needs of an efficient information retrieval in recent years in increased more then ever because of the frequent use of digital information in our life. We see a lot of work in the area of textual information but in multimedia information, we cannot find much progress. In text based information, new technology of data mining and data marts are now in working that were started from the basic concept of database some where in 1960. In image search and especially in image identification, computerized system at very initial stages. Even in the area of image search we cannot see much progress as in the case of text based search techniques. One main reason for this is the wide spread roots of image search where many area like artificial intelligence, statistics, image processing, pattern recognition play their role. Even human psychology and perception and cultural diversity also have their share for the design of a good and efficient image recognition and retrieval system. A new object based search technique is presented in this paper where object in the image are identified on the basis of their geometrical shapes and other features like color and texture where object-co-relation augments this search process. To be more focused on objects identification, simple images are selected for the work to reduce the role of segmentation in overall process however same technique can also be applied for other images.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1054895Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1700
 SMITH, J.R.a.C., S.-F. 1996: ÔÇÿA Fully Automated Content-Based Image Query System. -, in Editor (Ed.)^(Eds.): ÔÇÿBook A Fully Automated Content-Based Image Query System. - (1996, edn.), pp. 87-98
 SWAIN, M.a.B., D: ÔÇÿColor Indexing-, International journal of Computer 1991, 7, (11), pp. 11-3
 PICARD, R.: ÔÇÿA Society Of Models For Video and Image Libraries.-, in Editor (Ed.)^(Eds.): ÔÇÿBook A Society Of Models For Video and Image Libraries.- (1996, edn.), pp.
 AHUJA, N.a.R., A. : ÔÇÿ Mosaic Models For Texture-, IEEE Trans. Patt. Anal. , 1981, 3, (1)
 MODESTINO, J., FRIES, R., and VICKERS, A. : ÔÇÿTexture Discrimination Based Upon An Assumed Stochastic Texture Model.-, Ieee Trans. Patt. Anal. Mach. Intell, 1981, 3, (5), pp. 557-580.
 M. DAS, R.M.: ÔÇÿAutomatic Segmentation and Indexing In A Database Of Bird Images -, Proc. ICCV, 2001
 KANG, B.S.a.H.: ÔÇÿ DIRECT ANNOTATION. 2000. A Drag-and -Drop Strategy For Labeling Photos-. Proc. International Conference Information Visualization (Iv2000). London, England. pp. Pages
 SRIHARI, R., ZHANG, Z, and RAO, A.: ÔÇÿ Intelligent Indexing and Semantic Retrieval Of Multimodal Documents.-, Information Retrieval,, 2000, 2, pp. 245-275
 CHAD CARSON, S.B., HAYIT GREENSPAN , and JITENDRA MALIK. : ÔÇÿImage Segmentation Using Expectation-Maximization and Its A Lication To Image Querying -, In Ieee Transactions On Pattern Analysis and Machine Intelligence, 24, (8), pp. 1026-1038
 ENSER, P.: ÔÇÿQuery Analysis In A Visual Information Retrieval Context-, Document and Text Management, 1993., 1, (1), pp. 25-52
 WERTHEIMER, W.B.: ÔÇÿLaws Of Organization In Perceptual Forms (Partial Translation).- (Harcourt, Brace and Company, 1938. 1938)
 HARALICK, R.M.a.S.L.G.: ÔÇÿImage Segmentation Techniques-, Computer Vision, Graphics, & Image Processing, 1985, 29, (1), pp. 100- 132
 WOODS.., R.C.G.a.R.E.: ÔÇÿ Digital Image Processing, Second Edition- (Pearson Education Inc., 2002. 2002)
 FREIXENET J, X.M., D. RABA, J. MARTI, and X. CUFI: ÔÇÿYet Another Survey On Image Segmentation: Region and Boundary Information Integration,- in Editor (Ed.)^(Eds.): ÔÇÿBook Yet Another Survey On Image Segmentation: Region and Boundary Information Integration,- (2002, edn.)
 J.M, P.J.a.B.: ÔÇÿMultiscale Annealing For Real-Time Unsupervised Texture Segmentation,- in Editor (Ed.)^(Eds.): ÔÇÿBook Multiscale Annealing For Real-Time Unsupervised Texture Segmentation,- (1998., edn.), pp. 408-422
 BACH, J., FULLER, C., GUPTA, A., HAMPAPUR, A., HOROWITZ, B., JAIN, R., and SHU, C: ÔÇÿThe Virage Image Search Engine: An Open Framework For Image Management-, in Editor (Ed.)^(Eds.): ÔÇÿBook The Virage Image Search Engine: An Open Framework For Image Management- (1996, edn.), pp. 76-87
 PONCE J., Z.A., and HEBERT M.1996.: ÔÇÿObject Representation In Computer Vision-: ÔÇÿLecture Notes In Computer Science,- (1996), pp. 1144
 Springer Berlin / Heidelberg, ÔÇÿUsing a Fuzzy Object-Relational Database for Colour Image Retrieval-, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 2006