Hybrid index-based image search from the web

Abstract

Existing search techniques for retrieving images from the web store text-based and content-based features separately. They use structures like inverted-index, forward-index, document-term matrix, Tries, Prefix B-Tree, String B-Tree, etc. for text-based features and R-tree, SR-tree, K-B-D Tree, etc., for content-based features. We propose to use a hybrid indexing scheme which is more intuitive for hybrid image feature vectors and can be used to both store and query non-ordered discrete and continuous features simultaneously. Also, since most of the existing hybrid image search engines do not store two types of features together, they usually perform retrieval in two distinct steps, first finding results with only text-based information and later filtering results based on content-based information. In contrast, our approach of hybrid indexing supports retrieval in a single step. We introduce a k-nearest neighbour search algorithm for the hybrid indexing scheme used.