Feature Selection and Extraction for Content-Based Image Retrieval

E. Saravana Kumar; A. Sumathi; K. Latha

doi:https://doi.org/10.14445/22315373/IJMTT-V3I2P506

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 3 | Issue 2 | Year 2012 | Article Id. IJMTT-V3I2P506 | DOI : https://doi.org/10.14445/22315373/IJMTT-V3I2P506

Feature Selection and Extraction for Content-Based Image Retrieval

E. Saravana Kumar , A. Sumathi, K. Latha

Abstract

Content-Based Image Retrieval is a technique that utilizes the visual content of an image to search for similar images in large scale image databases. The visual content of an image represents the low-level features extracted from the image. These primarily constitute color, shape and texture features. The precision of image classification and image retrieval is mainly based on image feature extraction. More distinguished image features will yield better results in classification and retrieval process. Thus feature selection and feature extraction are the important tasks to be considered in image retrieval process. This paper aims to discuss about feature selection and an efficient method for feature extraction is proposed for image retrieval process.

Keywords

Content-Based Image Retrieval, Euclidean Distance Method, Relevance Feedback, Feature Vector

References

[1] Cai. D, He. X, and Han.J, “Isometric projection,” in Proc. AAAI, 2007, pp. 528–533.
[2] Chang. T and C.-C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process., vol.2, no. 4, pp. 429–441, Apr. 1993.
[3] Chen.Y, X.-S. Zhou, and T.-S. Huang, “One-class SVM for learning in image retrieval,” in Proc. IEEE Int. Conf. Image Processing, 2001, pp. 815–818.
[4] Guo.G, A. K. Jain, W.-Y. Ma, and H.-J. Zhang, “Learning similarity measure for natural image retrieval with relevance feedback,” IEEE Trans. Neural Netw., vol. 13, no. 7, pp. 811–820, Jul. 2002.
[5] Kherfi. M. L and D. Ziou, “Relevance feedback for CBIR: A new approach based on probabilistic feature weighting with positive and negative examples,” IEEE Trans. Image Process., vol. 15, no. 4, pp.1017–1030, Apr. 2006.
[6] Manjunath.B.S and W.-Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Trans. Pattern Anal. Mach. Intell., vol.18, no. 8, pp. 837–842, Aug. 1996.
[7] Oliva. A and Torralba. A “Modeling the shape of the scene: A holistic representation of the spatial envelope”, Int. J. Comp. Vision., vol.42, no. 3, pp. 145-175, 2001.
[8] Pass.G, R. Zabih, and J. Miller, “Comparing images using color coherence vectors,” in Proc. ACM Int. Conf. Multimedia, 1996, pp. 65–73.
[9] Swain.M.J and D. H. Ballard, “Color indexing,” Int. J. Comput. Vis., vol. 7, no. 1, pp. 11–32, 1991.
[10] Tao.D, X. Li, and S. J. Maybank, “Negative samples analysis in relevance feedback,” IEEE Trans. Knowl. Data Eng., vol. 19, pp. 568– 580,Apr. 2007.
[11] Tong.S and E. Chang, “Support vector machine active learning for image retrieval,” in Proc. ACM Int. Conf. Multimedia, 2001, pp.107– 118.
[12] Wang.J.Z, J. Li, and G. Wiederhold, “SIMPLIcity: Semantics-sensitive integrated matching for picture libraries,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 9, pp. 947–963, Sep. 2001.
[13] Wei Bian and Dacheng Tao,” Biased Discriminant Euclidean Embedding for Content Based Image Retrieval” IEEE Transactions on Image Processing, vol. 19, no. 2, Feb. 2010.
[14] [Online]. Available:http://sites.google.com/site/dctresearch/Home/content-basedimage-retrieval

Citation :

E. Saravana Kumar , A. Sumathi, K. Latha, "Feature Selection and Extraction for Content-Based Image Retrieval," International Journal of Mathematics Trends and Technology (IJMTT), vol. 3, no. 2, pp. 70-73, 2012. Crossref, https://doi.org/10.14445/22315373/IJMTT-V3I2P506