Author : Vineetha Deepti 1
Date of Publication :7th June 2016
Abstract: This paper presents a new approach to derive the image feature descriptor from the Error-diffusion based block truncation coding (EDBTC) compressed data stream. In the encoding step, EDBTC compresses an image block into corresponding quantizers and bitmap image using vector quantizer(VQ). Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the EDBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two EDBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the EDBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system. EDBTC method is extremely fast and the image quality achieved is comparable to the previous BTC method. This proposed system is implemented in MATLAB.
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[1] A. R. Backes, D. Casanova, and O. M. Bruno. Color texture analysis based on fractal descriptors. Pattern Recognition, 45(5):1984–1992, 2012.
[2] F. Bianconi, A. Fernandez, E. Gonzalez, D. Caride, and A. Calvino. Rotationinvariant colour texture classification through multilayer CCR. Pattern Recognition Letters, 30(8):765–773, 2009.
[3] G. J. Burghouts and J. M. Geusebroek. Material-specific adaptation of color invariant features. Pattern Recognition Letters, 30(3):306–313, 2009.
[4] B. J. L. Campana and E. J. Keogh. A compression-based distance measure for texture. Statistical Analy Data Mining, 3(6):381–398, 2010.
[5] J. Chen, S. Shan, C. He, G. Zhao, M. Pietikäinen, X. Chen, and W. Gao. WLD: a robust local image descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1705–1720, 2010.
[6] T. W. Chiang and T. W. Tsai. Content-based image retrieval via the multiresolution wavelet features of interest. Journal of Information Technology and Applications, 1(3):205–214, 2006.
[7] J. J. de Mesquita Sa Junior, P. C. Cortex, and A. R. Backes. Color texture classification using shortest paths in graphs. IEEE Transactions on Image Processing, 23(9):3751–3761, 2014.
[8] E. J. Delp and O. R. Mitchell. Image coding using block truncation coding. IEEE Transactions on Communications, 27(9):1335–1342, 1979.
[9] M. R. Gahroudi and M. R. Sarshar. Image retrieval based on texture and color method in BTC-VQ compressed domain. In 9th International Symposium on Signal Processing and Its Applications (ISSPA), pages 1–4, 2007.
[10] T. Guha and R. K. Ward. Image similarity using sparse representation and compression , distance. IEEE Transactions on Multimedia, 16(4):980–987, 2014.