Image retrieval using BDIP and BVLC moments

We propose two new texture features, block difference of inverse probabilities (BDIP) and block variation of local correlation coefficients (BVLC), for content-based image retrieval (CBIR) and then present an image retrieval method based on the combination of BDIP and BVLC moments. BDIP uses local probabilities in image blocks to measure an image's local brightness variations well. BVLC uses variations of local correlation coefficients in image blocks to measure local texture smoothness of an image well. Experimental results show that the presented retrieval method yields about 12% better performance in precision versus recall and about 0.13 in average normalized modified retrieval rank (ANMRR) than the method using wavelet moments.

[1]  Shih-Fu Chang,et al.  Transform features for texture classification and discrimination in large image databases , 1994, Proceedings of 1st International Conference on Image Processing.

[2]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[3]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[4]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[5]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[6]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[8]  Amarnath Gupta,et al.  Visual information retrieval , 1997, CACM.

[9]  Shih-Fu Chang,et al.  A fully automated content-based video search engine supporting spatiotemporal queries , 1998, IEEE Trans. Circuits Syst. Video Technol..

[10]  B. S. Manjunath,et al.  A comparison of wavelet transform features for texture image annotation , 1995, Proceedings., International Conference on Image Processing.

[11]  Nam Chul Kim,et al.  Valley operator for extracting sketch features: DIP , 1988 .

[12]  Shi-Kuo Chang,et al.  Principles of pictorial information systems design , 1988 .

[13]  Jitendra Malik,et al.  Blobworld: A System for Region-Based Image Indexing and Retrieval , 1999, VISUAL.

[14]  Jia-Lin Chen,et al.  Texture classification using QMF bank-based subband decomposition , 1992, CVGIP Graph. Model. Image Process..

[15]  K. S. Thyagarajan,et al.  A maximum likelihood approach to texture classification using wavelet transform , 1994, Proceedings of 1st International Conference on Image Processing.

[16]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[17]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Mohan S. Kankanhalli,et al.  Shape Measures for Content Based Image Retrieval: A Comparison , 1997, Inf. Process. Manag..

[19]  Kee Tung. Wong,et al.  Texture features for image classification and retrieval. , 2002 .

[20]  Nam Chul Kim,et al.  Extraction of texture regions using region-based local correlation , 2000, IS&T/SPIE Electronic Imaging.

[21]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.