Image Retrieval Based on Co-occurrence Matrix Using Block Classification Characteristics

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.