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Superpixel-based segmentation has been widely used as a primary prepossessing step to simplify the subsequent image processing tasks. Since determining the number of clusters is subjective and varies based on the type of image, the segmentation algorithm may provide over-segmented or under-segmented superpixels. This paper proposes an image segmentation method to improve the SLIC superpixel by region merging. It aims to improve the segmentation accuracy without defining a precise number of superls. The color difference between superpixels is employed as a homogeneity criterion for the merging process. The Berkeley dataset is used with different quantitative performance metrics to evaluate the proposed model's performance. Results obtained from probabilistic rand index (PRI), boundary recall, and under-segmentation error proved the ability of the proposed algorithm to provide comparable segmentation with a reduced number of clusters. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation is one of the crucial techniques in image processing. Even though numerous methods have been proposed in literature, it is still a challenge for the existing methods to produce steady satisfactory thresholds at manageable computational cost in segmenting images with various unknown properties. In this paper, a non-revisiting quantum-behaved particle swarm optimization (NrQPSO) algorithm is proposed to find the optimal multilevel thresholds for gray-level images. The proposed NrQPSO uses the non-revisiting scheme to avoid the re-evaluation of the evaluated solution candidates. To reduce the unnecessary computation cost, the NrQPSO provides an automatic stopping mechanism which is capable of gauging the progress of exploration and stops the algorithm rationally in a natural manner. For further improving the computation efficiency, the NrQPSO employs a meticulous solution search method to overcome the drawback of the existing QPSO algorithms using the original search methods. Performance of the NrQPSO is tested on the Berkeley segmentation data set. The experimental results have demonstrated that the NrQPSO can outperform the other state-of-the-art population-based thresholding methods in terms of efficiency, effectiveness and robustness; thus, the NrQPSO can be applied in real-time massive image processing. [ABSTRACT FROM AUTHOR]