An effective region-based image retrieval framework

Abstract

We present a region-based image retrieval framework that integrates efficient region-based representation in terms of storage and retrieval and effective on-line learning capability. The framework consists of methods for image segmentation and grouping, indexing using modified inverted file, relevance feedback, and continuous learning. By exploiting a vector quantization method, a compact region-based image representation is achieved. Based on this representation, an indexing scheme similar to the inverted file technology is proposed. In addition, it supports relevance feedback based on the vector model with a weighting scheme. A continuous learning strategy is also proposed to enable the system to self improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.