Object-Based Image Retrieval System Using Rough Set Approach

Ghali, Neveen I.; Abd-Elmonim, Wafaa G.; Hassanien, Aboul Ella

doi:10.1007/978-3-642-24693-7_10

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…A further improvement to the histon is proposed by Mushrif and Ray, 18 where a new histogram-like representation, named roughness index, is introduced. Recently, the roughness-based method has been used in different applications like image retrieval, 19 detection of moving objects, 20 color text segmentation, 21 and medical imaging. 22 However, this rough set-based methodology has not been explored for different features, rather than the RGB color representation.…”

Section: Introductionmentioning

confidence: 99%

Integration of color and texture cues in a rough set–based segmentation method

Lizarraga-Morales

Sánchez-Yáñez

Ayala-Ramírez

et al. 2014

J. Electron. Imaging

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Abstract. We propose the integration of color and texture cues as an improvement of a rough set-based segmentation approach, previously implemented using only color features. Whereas other methods ignore the information of neighboring pixels, the rough set-based approximations associate pixels locally. Additionally, our method takes into account pixel similarity in both color and texture features. Moreover, our approach does not require cluster initialization because the number of segments is determined automatically. The color cues correspond to the a and b channels of the CIELab color space. The texture features are computed using a standard deviation map. Experiments show that the synergistic integration of features in this framework results in better segmentation outcomes, in comparison with those obtained by other related and state-of-the-art methods. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 99%

Integration of color and texture cues in a rough set–based segmentation method

Lizarraga-Morales

Sánchez-Yáñez

Ayala-Ramírez

et al. 2014

J. Electron. Imaging

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“…This last step is accomplished only in the feature space, ignoring the spatial relationship between the different regions. Recently, the roughness index-based method has been used for different applications like image retrieval, 20 detection of moving objects, 21 color text segmentation, 22 and medical imaging. 23 Concerning the color features representation, it is known that the performance of a color-based segmentation method highly depends on the choice of the color space.…”

Section: Introductionmentioning

confidence: 99%

Improving a rough set theory-based segmentation approach using adaptable threshold selection and perceptual color spaces

Lizarraga-Morales

Sánchez-Yáñez

Ayala-Ramírez

et al. 2014

J. Electron. Imaging

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Abstract. We propose a color image segmentation approach based on rough set theory elements. Main contributions of the proposed approach are twofold. First, by using an adaptive threshold selection, the approach is automatically adjustable according to the image content. Second, a region-merging process, which takes into account both features and spatial relations of the resulting segments, lets us minimize over-segmentation issues. These two proposals allow our method to overcome some performance issues shown by previous rough set theory-based approaches. In addition, a study to determine the best suited color representation for our segmentation approach is carried out, determining that the best results are obtained using a perceptually uniform color space. A set of qualitative and quantitative tests over a comprehensive image database shows that the proposed method produces high-quality segmentation outcomes, better than those obtained using the previous rough set theory-based and standard segmentation approaches. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Feature Thresholding in Generalized Approximation Spaces

Małyszko

2015

Advances in Intelligent Systems and Computing

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Object-Based Image Retrieval System Using Rough Set Approach

Cited by 3 publications

References 21 publications

Integration of color and texture cues in a rough set–based segmentation method

Integration of color and texture cues in a rough set–based segmentation method

Improving a rough set theory-based segmentation approach using adaptable threshold selection and perceptual color spaces

Feature Thresholding in Generalized Approximation Spaces

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