Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Defect image classification and retrieval with MPEG-7 descriptors
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Browsing and searching MPEG-7 images using formal concept analysis
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
An intelligent user interface for browsing and searching MPEG-7 images using concept lattices
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
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According to the definition of the edge histogram descriptor (EHD) in MPEG-7, one can easily generate an extra histogram bin from the 5-bin local edge histogram of each 4 × 4 sub-image. This extra histogram bin defines the ratio of the non-edge area (i.e., monotonous region) in the sub-image. Forming a feature vector with 6 edge/non-edge types, we can generate 33 different feature vectors (or 33 × 6 = 198 feature elements) including 16 vectors from 4 × 4 sub-images, 1 vector from a global histogram, 13 vectors from semi-global histograms, 1 vector from entropy, and 2 vectors from centers of gravity. A statistical hypothesis testing is employed to see which feature vectors/elements are most informative to differentiate different image classes. Experimental results show that non-edge and entropy features are the most informative features among all 33/198 feature vectors/elements.