An improved feature vector for content-based image retrieval in DCT domain

International audienceThis paper proposes an improved approach for content-based image retrieval in Discrete Cosine Transform domain. For each 4x4 DCT block, we calculate the statistical information of three groups of AC coefficients and propose to use these information to form the AC-Pattern and use DC coefficients of neighboring blocks to construct DC-Pattern. The histograms of these two patterns are constructed and their selections are concatenated as feature descriptor. Similarity between the feature descriptors is measured by chi-squared distance. Experiments executed on widely used face and texture databases show that better performance can be observed with the proposal compared with other classical method and state-of-the-art approaches