Kiranyaz S.Pulkkinen J.İnce, TürkerGabbouj, Moncef2023-06-162023-06-1620119.78E+121522-4880https://doi.org/10.1109/ICIP.2011.6116508https://hdl.handle.net/20.500.14365/3549IEEE;IEEE Signal Processing Society2011 18th IEEE International Conference on Image Processing, ICIP 2011 -- 11 September 2011 through 14 September 2011 -- Brussels -- 88213Low-level features (also called descriptors) play a central role in content-based image retrieval (CBIR) systems. Features are various types of information extracted from the content and represent some of its characteristics or signatures. However, especially the (low-level) features, which can be extracted automatically usually lack the discrimination power needed for accurate description of the image content and may lead to a poor retrieval performance. In order to efficiently address this problem, in this paper we propose a multidimensional evolutionary feature synthesis technique, which seeks for the optimal linear and non-linear operators so as to synthesize highly discriminative set of features in an optimal dimension. The optimality therein is sought by the multi-dimensional particle swarm optimization method along with the fractional global-best formation technique. Clustering and CBIR experiments where the proposed feature synthesizer is evolved using only the minority of the image database, demonstrate a significant performance improvement and exhibit a major discrimination between the features of different classes. © 2011 IEEE.eninfo:eu-repo/semantics/closedAccessContent-based image retrievalEvolutionary feature synthesismulti-dimensional particle swarm optimizationContent based image retrievalDescriptorsFeature synthesisImage contentImage databaseLow-level featuresOptimalityParticle swarmParticle swarm optimization methodPerformance improvementsRetrieval performanceContent based retrievalImage processingMathematical operatorsParticle swarm optimization (PSO)Search enginesConference Object10.1109/ICIP.2011.61165082-s2.0-84856248686