Image Retrieval Using Modified Color Variation Co-occurrence Matrix
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Implementation of an image retrieval system using wavelet decomposition and gradient variation
WSEAS Transactions on Computers
Wavelet transform on pixel distribution of rows & column of BMP image for CBIR
Proceedings of the International Conference on Advances in Computing, Communication and Control
Moments of pixel distribution of BMP image for CBIR
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
HEp-2 cell classification in indirect immunofluorescence image
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Color and texture features for content based image retrieval system
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
An automatic body ROI determination for 3d visualization of a fetal ultrasound volume
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Image retrieval using spatial color and edge detection
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Face detection using sketch operators and vertical symmetry
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
A new content-based image retrieval technique using color and texture information
Computers and Electrical Engineering
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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.