Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Features for image retrieval: an experimental comparison
Information Retrieval
Visual search reranking via adaptive particle swarm optimization
Pattern Recognition
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The paper presents a method for content-based image retrieval based on an evolutionary algorithm. Stochastic approaches have been applied with success in several optimization problems thanks to their capability to explore the solution space, in particular in complex, multidimensional space, avoiding local maxima of the target function. Here, we show how a Particle Swarm Optimization algorithm approprietely designed to exploit the user feedback in CBIR may outperform traditional Relevance Feedback approaches, showing a much higher precision/recall thanks to the capability of navigating the feature space and to move the swarm towards the most appropriate image cluster.