Semantic Image Retrieval Using Point-Set Topology and the Ant Sleeping Model

Abstract

Due to its widespread practical applications to image database management, semantic image retrieval has received a lot of attention in past decades. In particular, relevance feedback-based methods have been a popular approach toward bridging the gap between low-level features and high-level semantic concepts. Nature-inspired algorithms have shown a lot of potential to solve problems which are complex and require discovering of patterns under changing environment. However, lack of sound mathematical foundations has been considered a drawback toward better analysis of these algorithms. In this chapter, we propose a novel general topological model for semantic image retrieval using relevance feedback. We use point-set topology to develop mathematical constructs for modeling the semantic retrieval. In particular, we develop an image retrieval algorithm based on the ant sleeping model and extend the topological model to analyze it. Through experiments we show that our algorithm performs well in indexing an image database for relevance feedback. With our indexing procedure, the average response time to access image results from a storage device is lower when compared to vector quantization techniques. We also evaluate our algorithm, theoretically and empirically, against PicSOM (a CBIR system based on relevance feedback). Our ASM-based technique shows a very efficient retrieval performance using relevance feedback.