RGB-D image kernel fuzzy clustering based on improved chimp optimization algorithm
With the help of a low-cost depth sensor,an RGB-D image with depth and color synchronization is produced.Aiming at the difficulties of RGB-D image segmentation and the problems of slow convergence speed,low accuracy and a susceptibility to getting trapped in local optima of chimp optimization algorithm.A RGB-D image kernel fuzzy clustering based on improved chimp optimization algorithm(IChOA)is proposed.Firstly,six feature subsets are extracted from RGB-D images.Secondly,Levy flight strategy and Nonlinear Inertia Weight are introduced to transform ChOA.Finally,IChOA is used to kernel fuzzy clustering on six feature subsets,and multiple optimal clusters are obtained.Then the aggregating superpixels method is used to segment multiple optimal clusters in different combinations,and generated the final segmentation result.The experiment is carried out by using the indoor image dataset of NYU depth V2,and compared with some existing segmentation methods:threshold segmentation,Fuzzy subspace clustering,Residual-driven Fuzzy C-Means,hard C-means,fuzzy C-means,kernel fuzzy clustering,chaotic kbest gravitational search algorithm and random Henry gas solubility optimization algorithm.The results show that the proposed RGB-D segmentation algorithm is superior to the compared algorithms.
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