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An CCBIR System using Associative Mining Techniques for Semantic Search Engines

K. Venkatasalam

Abstract


Due to the increase in the size of World Wide Web, Content Based Retrieval becomes more challenging and also it provides lot of irrelevant results. We propose a new Concept and Content Based Image Retrieval technique to provide exact results. We use Associative mining technique with the semantic concepts. Our methodology indexes the images according to semantic concepts and generates association which will be used for Image retrieval. We extract the high level concepts and low level features. The extracted feature vectors are indexed to a semantic concept, and we generate association rules, using which the retrieval is done. We use both the visual and texture features to represent the semantic concept. This CCBIR is very help full in both data collection and modeling large scale image data bases.

Keywords


Semantics, CBIR, Associative Technique, Semantic Search Engines.

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References


L. J. Latecki, R. Lakamper, and U. Eckhardt, “Shape descriptors for non-rigid shapes with a single closed contour,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000, pp. 424–429.

S. Belongie, J. Malik, and J. Puzicha, “Shape matching and object recognition using shape contexts,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 4, pp. 509–522, Apr. 2002.

H. Ling and D. W. Jacobs, “Shape classification using the inner-distance,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 2, pp. 286–299, Feb. 2007.

C. Rao, A. Yilmaz, and M. Shah, “View-invariant representation and recognition of actions,” Int. J. Comput. Vis., vol. 50, no. 2, pp. 203–226,2002.

Y.Wang, H. Jiang, M. Drew, L. Ze-Nian, and G. Mori, “Unsupervised discovery of action classes,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006, pp. 1654–1661.

D. Sharvit, J. Chan, H. Tek, and B. B. Kimia, “Symmetry-based indexing of image databases,” J. Vis. Commun. Image Represent., vol. 9, no. 4, pp. 366–380, 1998.

T. B. Sebastian, P. N. Klein, and B. B. Kimia, “Recognition of shapes by editing their shock graphs,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 5, pp. 550–571, May 2004.

B. Leibe and B. Schiele, “Analyzing appearance and contour based methods for object categorization,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2003.

S. Biswas, G. Aggarwal, and R. Chellappa, “Efficient indexing for articulation invariant shape matching and retrieval,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007, pp. 1–8.

G. Mori and J. Malik, “Recognizing objects in adversarial clutter: Breaking a visual captcha,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2003, pp. 134–141.

Z. Tu and A. L. Yuille, “Shape matching and recognition: Using generative models and informative features,” in Proc. Eur. Conf. Computer Vision, 2004, pp. 195–209.


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