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Gaussian Density and HOG with Content Based Image Retrieval System – A New Approach
Content-based image retrieval (CBIR) uses the contents of images to characterize and contact the images. This paper focus on retrieving the image by separating images into its three color mechanism R, G and B and for that Discrete Wavelet Transformation is applied. Then Wavelet based Generalized Gaussian Density (GGD) is practical which is used for modeling the coefficients from the wavelet transforms. After that it is agreed to Histogram of Oriented Gradient (HOG) for extracting its characteristic vectors with Relevant Feedback technique is used. The performance of this approach is calculated by exactness and it confirms that this method is wellorganized for image retrieval.
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[1] M. K. Mandal, T. Aboulnasr, and S. Panchanathan,, “Image Indexing Using Moments and Wavelets”, IEEE Transactions on Consumer Electronics, Vol. 42, No. 3, August 1996V. Gudivada and V. Raghavan. Content-based image retrieval systems. IEEE Computer, 28(9):18 – 22, September 1995.
[2] J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack. Efficient color histogram indexing for quadratic form distance functions. IEEE Transactions on Pattern Analysis and Machine Intelligenc (PAMI), 17(7):729–736, 1995.
[3] G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. In ACM International Conference on Multimedia, pages 65–73, Boston, MA, Nov. 1996.
[4] B. S. Manjunath and W. Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Trans. Pattern Recognit. Machine Intell., vol. 18, pp. 837–842, Aug. 1996.
[5] G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture characterization from discrete wavelet representations,” IEEE Trans. Image Processing, vol. 8, pp. 592–598, Apr. 1999.
[6] T. Randen and J. H. Husoy, “Filtering for texture classification: A comparative study,” IEEE Trans. Pattern Recognit. Machine Intell., vol. 21, pp. 291–310, 1999..
[7] JPEG Committee. JPEG home page. (Online)
[8] C. Jacobs, A. Finkelstein, and D. Salesin, “Fast multiresolution image querying,” in Proc. SIGGRAPH Computer Graphics, Los Angeles, CA, 1995, pp. 278–280.
[9] M. Do, S. Ayer, and M. Vetterli, “Invariant image retrieval using wavelet maxima moment,” in Proc. 3rd Int. Conf. Visual Information Information Systems, 1999, pp. 451–458.
[10] S. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Recognit. Mach. Intell., vol. 11, no. 7, pp. 674–693, Jul. 1989.
[11] P.S.Suhasini, Dr. K.sri rama krishna, Dr. I. V. Murali krishna,”cbir using color histogram processing” Journal of Theoretical and Applied Information Technology 2009.
[12] Vibha Bhandari and Sandeep B.Patil,CBIR Using DCT for Feature Vector Generation,. International Journal of Application or Innovation in Engineering & Management, Volume 1, Issue 2, October 2012.
[13] A. Smeulders, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349- 1380, May. 2000.
[14] J. Caicedo, F. Gonzalez, E. Romero, E. triana, “Design of a Medical Image Database with Content-Based Retrieval Capabilities,” In Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology, Santiago, Chile, December 17-19, 2007.
[15] G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture characterization from discrete wavelet representations,” IEEE Trans. Image Processing, vol. 8, pp. 592–598, Apr. 1999.
[16] N. Vasconcelos and A. Lippman, “A unifying view of image similarity,” in Proc. IEEE Int. Conf. Pattern Recognition (ICPR), Barcelona, Spain, 2000.
[17] G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture characterization from discrete wavelet representations,” IEEE Trans. Image Process., vol. 8, no. 4, pp. 592–598, Apr. 1999.
[18] M. N. Do and M. Vetterli, “Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance,” IEEE Trans. Image Process., vol. 11, no. 2, pp. 146–158, Feb. 2002.
[19] Pushpa B. Patil, Manesh B. Kokare(2011), “Relevance Feedback In Content Based Image Retrieval: A Review”, Journal Of Applied Computer Science & Mathematics, Vol.5, No. 10 .
[20] Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2005)
[21] Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Proc. of Computer Vision and Pattern Recognition, Washington, pp. 66–75 (2004).
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