Integrating image clustering and memory indexing for large scale content-based image retrieval

Content-Based Image Retrieval (CBIR) is an important research topic of information retrieval, involved in computer graphics, image processing, data mining and pattern recognizing. To make content-based image retrieval suitable large-scale image database, we develop an effective dynamic hierarchical clustering index scheme. Although this system uses a hierarchical clustering technology, with the increasing in the number of cluster centers, it is slow to find the centers, and it becomes a system performance bottleneck. In this paper, content features of image memory indexing is built. This method effectively improves the retrieval speed without loss of the precision. Moreover, the clustering model was improved, integrating the content features and textual features of image, which greatly improve the accuracy of the clustering, thus significantly improves the system precision.