Improving High-Dimensional Indexing with Heuristics for Content-Based Image Retrieval

Most high-dimensional indexing structures proposed for similarity query in content-based image retrieval (CBIR) systems are tree-structured. The quality of a high-dimensional tree-structured index is mainly determined by its insertion algorithm. Our approach focuses on an important phase in insertion, that is, the tree descending phase, when the tree is explored to nd a host node to accommodate the vector to be inserted. We propose to integrate a heuristic algorithm in tree descending in order to nd a better host node and thus improve the quality of the resulting index. A heuristic criteria for child selection has been developed , which takes into account both the similarity-based distance and the radius-increasing of the potential host node. Our approach has been implemented and tested on an image database. Our experiments show that the proposed approach can improve the quality of high-dimensional indices without much run-time overhead.