International Journal of Computer Vision
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Techniques and Systems for Image and Video Retrieval
IEEE Transactions on Knowledge and Data Engineering
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Recognition Letters
IEEE Transactions on Image Processing
Color image indexing using BTC
IEEE Transactions on Image Processing
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
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A new method of content-based image retrieval is presented that uses the color co-occurrence matrix that is adaptive to the classification characteristics of the image blocks. In the proposed method, the color feature vectors are extracted according to the characteristics of the block classification after dividing the image into blocks with a fixed size. The divided blocks are then classified as either luminance or color blocks depending on the average saturation of the block in the HSI (hue, saturation, and intensity) domain. Thereafter, the color feature vectors are extracted by calculating the co-occurrence matrix of a block average intensity for the luminance blocks and the co-occurrence matrix of a block average hue and saturation for the color blocks. In addition, block directional pattern feature vectors are extracted by calculating histograms after directional gradient classification of the intensity. Experimental results show that the proposed method can outperform conventional methods as regards a precision and a feature vector dimension.