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Color Image Segmentation

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Advanced Color Image Processing and Analysis

Abstract

Splitting an input image into connected sets of pixels is the purpose of image segmentation. The resulting sets, called regions, are defined based on visual properties extracted by local features. To reduce the gap between the computed segmentation and the one expected by the user, these properties tend to embed the perceived complexity of the regions and sometimes their spatial relationship as well. Therefore, we developed different segmentation approaches, sweeping from classical color texture to recent color fractal features, in order to express this visual complexity and show how it can be used to express homogeneity, distances, and similarity measures. We present several segmentation algorithms, like JSEG and color structure code (CSC), and provide examples for different parameter settings of features and algorithms. The now classical segmentation approaches, like pyramidal segmentation and watershed, are also presented and discussed, as well as the graph-based approaches. For the active contour approach, a diffusion model for color images is proposed. Before drawing the conclusions, we talk about segmentation performance evaluation, including the concepts of closed-loop segmentation, supervised segmentation and quality metrics, i.e., the criteria for assessing the quality of an image segmentation approach. An extensive list of references that covers most of the relevant related literature is provided.

Colors, like features, follow the changes of the emotions

Pablo Picasso

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Notes

  1. 1.

    The process must be absolutely continuous with respect to the ℝ d—Lebesgue measure.

  2. 2.

    To reduce the computational cost, Deng proposes a reduced list of values to try: \([-0.6,-0.4,-0.2, 0, 0.2, 0.4]\).

  3. 3.

    i arbitrary, but large.

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Acknowledgment

We would like to thank Martin Druon, Audrey Ledoux, and Julien Dombre (XLIM-SIC UMR CNRS 6172, Université de Poitiers, France), Diana Stoica and Alexandru Căliman (MIV Imaging Venture, Transilvania University, Braşov, România) for the results they provided and for the fruitful discussions. Image “angel” is courtesy of Centre d’Etudes Supérieurs de Civilisation Médiévale (CESCM), UMR 6223, Poitiers, France, while the melanoma image is courtesy of Dermnet Skin Disease Image Atlas, http://www.dermnet.com.

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Authors and Affiliations

  1. MIV Imaging Venture Laboratory, Department of Electronics and Computers, Transilvania University Braşov, Brasov, România

    Mihai Ivanovici

  2. Laboratory XLIM-SIC, UMR CNRS 7252, University of Poitiers, Poitiers, France

    Noël Richard

  3. Computervisualistik, Universität Koblenz-Landau, D-56070, Koblenz, Germany

    Dietrich Paulus

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  1. Mihai Ivanovici
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  2. Noël Richard
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  3. Dietrich Paulus
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Correspondence to Mihai Ivanovici .

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  1. Laboratoire XLIM-SIC, Dépt. SIC, Université de Poitiers, 11 bd. Marie et Pierre Curie, Futuroscope CX, 86962, France

    Christine Fernandez-Maloigne

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Ivanovici, M., Richard, N., Paulus, D. (2013). Color Image Segmentation. In: Fernandez-Maloigne, C. (eds) Advanced Color Image Processing and Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6190-7_8

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