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Color image segmentation using adaptive color quantization and multiresolution texture characterization

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Abstract

This paper presents a new hybrid color image segmentation approach, which attempts two different transforms for texture representation and extraction. The 2-D discrete wavelet transform that can express the variance in frequency and direction of textures, and the contourlet transform that represents boundaries even more accurately are applied in our algorithm. The whole segmentation algorithm contains three stages. First, an adaptive color quantization scheme is utilized to obtain a coarse image representation. Then, the tiny regions are combined based on color information. Third, the proposed energy transform function is used as a criterion for image segmentation. The motivation of the proposed method is to obtain the complete and significant objects in the image. Ultimately, according to our experiments on the Berkeley segmentation database, our techniques have more reasonable and robust results than other two widely adopted image segmentation algorithms, and our method with contourlet transform has better performance than wavelet transform.

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Authors and Affiliations

  1. Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau SAR, People’s Republic of China

    Ning-Yu An & Chi-Man Pun

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  1. Ning-Yu An
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  2. Chi-Man Pun
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Correspondence to Chi-Man Pun.

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An, NY., Pun, CM. Color image segmentation using adaptive color quantization and multiresolution texture characterization. SIViP 8, 943–954 (2014). https://doi.org/10.1007/s11760-012-0340-2

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  • DOI: https://doi.org/10.1007/s11760-012-0340-2

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