Color Histogram and DBC Co-Occurrence Matrix for Content Based Image Retrieval
Автор: K. Prasanthi Jasmine, P. Rajesh Kumar
Журнал: International Journal of Information Engineering and Electronic Business(IJIEEB) @ijieeb
Статья в выпуске: 6 vol.6, 2014 года.
Бесплатный доступ
This paper presents the integration of color histogram and DBC co-occurrence matrix for content based image retrieval. The exit DBC collect the directional edges which are calculated by applying the first-order derivatives in 0º, 45º, 90º and 135º directions. The feature vector length of DBC for a particular direction is 512 which are more for image retrieval. To avoid this problem, we collect the directional edges by excluding the center pixel and further applied the rotation invariant property. Further, we calculated the co-occurrence matrix to form the feature vector. Finally, the HSV color histogram and the DBC co-occurrence matrix are integrated to form the feature database. The retrieval results of the proposed method have been tested by conducting three experiments on Brodatz, MIT VisTex texture databases and Corel-1000 natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, DBC and other transform domain features.
Color, Directional Binary Code, Texture, Pattern Recognition, Feature Extraction, Local Binary Patterns, Image Retrieval
Короткий адрес: https://sciup.org/15013317
IDR: 15013317
Список литературы Color Histogram and DBC Co-Occurrence Matrix for Content Based Image Retrieval
- Y. Rui and T. S. Huang, Image retrieval: Current techniques, promising directions and open issues, J.. Vis. Commun. Image Represent., 10 (1999) 39–62.
- A. W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, Content-based image retrieval at the end of the early years, IEEE Trans. Pattern Anal. Mach. Intell., 22 (12) 1349–1380, 2000.
- M. Kokare, B. N. Chatterji, P. K. Biswas, A survey on current content based image retrieval methods, IETE J. Res., 48 (3&4) 261–271, 2002.
- Ying Liu, Dengsheng Zhang, Guojun Lu, Wei-Ying Ma, Asurvey of content-based image retrieval with high-level semantics, Elsevier J. Pattern Recognition, 40, 262-282, 2007.
- M. J. Swain and D. H. Ballar, Indexing via color histograms, Proc. 3rd Int. Conf. Computer Vision, Rochester Univ., NY, (1991) 11–32.
- M. Stricker and M. Oreng, Similarity of color images, Proc. SPIE, Storage and Retrieval for Image and Video Databases, (1995) 381–392.
- G. Pass, R. Zabih, and J. Miller, Comparing images using color coherence vectors, Proc. 4th ACM Multimedia Conf., Boston, Massachusetts, US, (1997) 65–73.
- J. Huang, S. R. Kumar, and M. Mitra, Combining supervised learning with color correlograms for content-based image retrieval, Proc. 5th ACM Multimedia Conf., (1997) 325–334.
- Z. M. Lu and H. Burkhardt, Colour image retrieval based on DCT domain vector quantization index histograms, J. Electron. Lett., 41 (17) (2005) 29–30.
- R.M. Haralick, K. Shangmugam, I. Dinstein, Textural feature for image classification, IEEE Trans. Syst. Man Cybern. SMC-3 (6) (1973) 610–621.
- G. Cross, A. Jain, Markov random field texture models, IEEE Trans. Pattern Anal. Mach. Intell. 5 (1) (1983) 25–39.
- J. Mao, A. Jain, Texture classification and segmentation using multi-resolution simultaneous autoregressive models, Pattern Recognition 25 (2) (1992) 173–188.
- F. Liu, R. Picard, Periodicity, directionality, and randomness: wold features for image modeling and retrieval, IEEE Trans. Pattern Anal. Mach. Intell. 18 (7) (1996) 722–733.
- B.S. Manjunath, W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Trans. Pattern Anal. Mach. Intell. 18 (8) (1996) 837–842.
- J. Han, K.-K. Ma, Rotation-invariant and scale-invariant Gabor features for texture image retrieval, Image Vision Comput. 25 (2007) 1474–1481.
- T. Chang, C.C. Jay Kuo, Texture analysis and classification with tree-structured wavelet transform, IEEE Trans. Image Process. 2 (4) (1993) 429–441.
- A. Laine, J. Fan, Texture classification by wavelet packet signatures, IEEE Trans. Pattern Anal. Mach. Intell. 11 (15) (1993) 1186–1191.
- M. Subrahmanyam, A. B. Gonde and R. P. Maheshwari, Color and Texture Features for Image Indexing and Retrieval, IEEE Int. Advance Computing Conf., Patial, India, (2009) 1411-1416.
- Subrahmanyam Murala, R. P. Maheshwari, R. Balasubramanian, A Correlogram Algorithm for Image Indexing and Retrieval Using Wavelet and Rotated Wavelet Filters, Int. J. Signal and Imaging Systems Engineering.
- T. Ojala, M. Pietikainen, D. Harwood, A comparative sudy of texture measures with classification based on feature distributions, Elsevier J. Pattern Recognition, 29 (1): 51-59, 1996.
- T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Mach. Intell., 24 (7): 971-987, 2002.
- M. Pietikainen, T. Ojala, T. Scruggs, K. W. Bowyer, C. Jin, K. Hoffman, J. Marques, M. Jacsik, W. Worek, Overview of the face recognition using feature distributions, Elsevier J. Pattern Recognition, 33 (1): 43-52, 2000.
- T. Ahonen, A. Hadid, M. Pietikainen, Face description with local binary patterns: Applications to face recognition, IEEE Trans. Pattern Anal. Mach. Intell., 28 (12): 2037-2041, 2006.
- G. Zhao, M. Pietikainen, Dynamic texture recognition using local binary patterns with an application to facial expressions, IEEE Trans. Pattern Anal. Mach. Intell., 29 (6): 915-928, 2007.
- M. Heikkil;a, M. Pietikainen, A texture based method for modeling the background and detecting moving objects, IEEE Trans. Pattern Anal. Mach. Intell., 28 (4): 657-662, 2006.
- X. Huang, S. Z. Li, Y. Wang, Shape localization based on statistical method using extended local binary patterns, Proc. Inter. Conf. Image and Graphics, 184-187, 2004.
- M. Heikkila, M. Pietikainen, C. Schmid, Description of interest regions with local binary patterns, Elsevie J. Pattern recognition, 42: 425-436, 2009.
- M. Li, R. C. Staunton, Optimum Gabor filter design and local binary patterns for texture segmentation, Elsevie J. Pattern recognition, 29: 664-672, 2008.
- B. Zhang, Y. Gao, S. Zhao, J. Liu, Local derivative pattern versus local binary pattern: Face recognition with higher-order local pattern descriptor, IEEE Trans. Image Proc., 19 (2): 533-544, 2010.
- B. Zhang, L. Zhang, D. Zhang, L. Shen, Directional binary code with application to PolyU near-infrared face database, Pattern Recognition Letters 31 (2010) 2337–2344.
- A. Abdullah, R. C. Veltkamp and M. A. Wiering, Fixed Partitioning and salient points with MPEG-7 cluster correlogram for image categorization, Pattern Recognition, 43, (2010) 650-662.
- P. Brodatz, Textures: A Photographic Album for Artists and Designers, New York: Dover, 1996.
- University of Southern California, Signal and Image Processing Institute, Rotated Textures. [Online]. Available: http://sipi.usc.edu/database/.
- MIT Vision and Modeling Group, Vision Texture. [Online]. Available: http://vismod.www.media.mit.edu.
- Corel-1000 natural image database. [Online]. Available: http://wang.ist.psu.edu/docs/related.shtml.
Статья научная
