Fast indexing and searching for content-based image retrieval

This paper presents a general approach to fast image indexing and searching for content-based image retrieval on a network of workstation clusters. Three primary issues in image retrieval are discussed: image feature extraction and representation, similarity measure, and searching methods. A wavelet based image feature extraction scheme is introduced to represent images with multiple features such as colours, textures and shapes. In addition, a feature component code is proposed to facilitate a dynamic image indexing scheme where images are queried by different features or combinations. Furthermore, the relevance feedback technique for information retrieval is used to convert image feature vectors to weight-term vectors for efficient searching.