Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Trends in Intelligent Systems and Computer Engineering
Trends in Intelligent Systems and Computer Engineering
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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In this paper, we propose an automatic image annotation approach for region labeling that takes advantage of both context and semantics present in segmented images. The proposed approach is based on multi-class K-nearest neighbor, k-means and particle swarm optimization (PSO) algorithms for feature weighting, in conjunction with normalized cuts-based image segmentation technique. This hybrid approach refines the output of multi-class classification that is based on the usage of K-nearest neighbor classifier for automatically labeling images regions from different classes. Each input image is segmented using the normalized cuts segmentation algorithm then a descriptor created for each segment. The PSO algorithm is employed as a search strategy for identifying an optimal feature subset. Extensive experimental results demonstrate that the proposed approach provides an increase in accuracy of annotation performance by about 40%, via applying PSO models, compared to having no PSO models applied, for the used dataset.