An adaptive unsupervised approach toward pixel clustering and color image segmentation

  • Authors:

  • Zhiding Yu;Oscar C. Au;Ruobing Zou;Weiyu Yu;Jing Tian

  • Affiliations:

  • Department of Electronic & Computer Engineering, the Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong SAR, PR China;Department of Electronic & Computer Engineering, the Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong SAR, PR China;School of Electronic & Information Engineering, South China University of Technology, Guangzhou, 510641, PR China;School of Electronic & Information Engineering, South China University of Technology, Guangzhou, 510641, PR China;School of Electronic & Information Engineering, South China University of Technology, Guangzhou, 510641, PR China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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Abstract

This paper proposes an adaptive unsupervised scheme that could find diverse applications in pattern recognition as well as in computer vision, particularly in color image segmentation. The algorithm, named Ant Colony-Fuzzy C-means Hybrid Algorithm (AFHA), adaptively clusters image pixels viewed as three dimensional data pieces in the RGB color space. The Ant System (AS) algorithm is applied for intelligent initialization of cluster centroids, which endows clustering with adaptivity. Considering algorithmic efficiency, an ant subsampling step is performed to reduce computational complexity while keeping the clustering performance close to original one. Experimental results have demonstrated AFHA clustering's advantage of smaller distortion and more balanced cluster centroid distribution over FCM with random and uniform initialization. Quantitative comparisons with the X-means algorithm also show that AFHA makes a better pre-segmentation scheme over X-means. We further extend its application to natural image segmentation, taking into account the spatial information and conducting merging steps in the image space. Extensive tests were taken to examine the performance of the proposed scheme. Results indicate that compared with classical segmentation algorithms such as mean shift and normalized cut, our method could generate reasonably good or better image partitioning, which illustrates the method's practical value.