Study of Image Retrieval Based on Feature Vectors in Compressed Domain
Daidi Zhong, Irek Defee · 2006
An image retrieval method is proposed in this article, exploiting information of frequency components in compressed blocks. In this method images are first processed with block transform and quantization. Subsequently, the binary feature vector (BFV) is formulated to represent the local visual information. Special histograms are generated next based on BFV vectors providing statistical description of distribution of BFV vectors. The BFV concept is then extended to ternary feature vector (TFV). The BFV and TFV histograms are used for the image database retrieval. Three different feature vector schemes are proposed and the performances are investigated. Good retrieval results are obtained for standard public face image database