一种局部受限的规则聚类超像素算法

doi:10.3969/j.issn.1001-2400.2016.03.017

Abstract

Abstract:

It is difficult to obtain superpixels which are compact and adhere well to image boundary by traditional superpixel algorithms, because of their high complexity. This research proposes a new superpixel algorirhm of Locally-Restricted Regular Clustering (LRRC) for overcoming those difficulties. This algorithm is based on the k-means algorithm, and adopts the LRRC method and a class combination strategy to produce superpixels with equal and regular sizes. In clustering, both pixel color and position features are taken into account, and the logarithm mechanisim is introduced to balance the differences of their values. Through the special color distance filtering process the boundaries of superpixels are smoothed more effectively. Simulation results show that the LRRC algorithm is simple for use, efficient for computation, and can get a high boundary recall and a low under-segmentation error. When the number of partition superpixels is fairly large, the performance of the LRRC algorithm is better than other powerful superpixel algorithms in available.

Key words: k-means clustering, superpixels, feature distance, logarithm mechanisim, image segmentation

CLC Number:

TP391

WANG Yunfei;BI Duyan;LIU Huawei;LIU Ling;ZHAO Xiaolin. Locally-restricted regular clustering superpixel algorithm[J].Journal of Xidian University, 2016, 43(3): 95-100.

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Locally-restricted regular clustering superpixel algorithm

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