Publication

Abstract : The proposed approach avoids the semantic gap in image retrieval by combining automatic relevance feedback and a modified stochastic algorithm. A visual feature database is constructed from the image database, using combined feature vector. Very few fast-computable features are included in this step. The user selects the query image, and based on that, the system ranks the whole dataset. The nearest images are retrieved and the first automatic relevance feedback is generated. The combined similarity of textual and visual feature space using Latent Semantic Indexing is evaluated and the images are labelled as relevant or irrelevant. The feedback drives a feature re-weighting process and is routed to the particle swarm optimizer. Instead of classical swarm update approach, the swarm is split, for each swarm to perform the search in parallel, thereby increasing the performance of the system. It provides a powerful optimization tool and an effective space exploration mechanism. The proposed approach aims to achieve the following goals without any human interaction - to cluster relevant images using meta-heuristics and to dynamically modify the feature space by feeding automatic relevance feedback.