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.
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