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A Comparative Study on EM Algorithms for Color-Texture Image Segmentation

Published: 06 July 2016 Publication History

Abstract

Expectation-Maximization (EM) algorithm has been thoroughly studied in the maximum likelihood estimate of model parameters for statistical learning. Albeit EM algorithms are exploited to the nature of a variety of problems, they are commonly faced with operational difficulty in practice, due to its convergence of local maxima. The actual performance of different EM variants are seldom evaluated to resolve the same application-specific problem, for example image segmentation. In this work, we have conducted a comparative study on different EM variants. To more visually compare them, we employ the EM variants into a color-texture image segmentation algorithm. We first evaluated the effectiveness of several EM variants using the log-likelihood and Bayesian Information Criterion on the image data. Then the EM variants are used for color quantization in the framework of the color-texture segmentation algorithm to assess the performance of them. The local maxima problem of the EM algorithm is also studied by the image segmentation results.

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ICCCNT '16: Proceedings of the 7th International Conference on Computing Communication and Networking Technologies
July 2016
262 pages
ISBN:9781450341790
DOI:10.1145/2967878
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • University of North Texas: University of North Texas

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Association for Computing Machinery

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Published: 06 July 2016

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Author Tags

  1. Expectation-Maximization
  2. clustering
  3. color-texture image segmentation
  4. evaluation

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ICCCNT '16

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ICCCNT '16 Paper Acceptance Rate 48 of 101 submissions, 48%;
Overall Acceptance Rate 48 of 101 submissions, 48%

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