Color Texture Segmentation Using Color Transform and Feature Distributions

Weng, Shiuh-Ku; Kuo, Chung-Ming; Kang, Wei-Cung

doi:10.1093/ietisy/e90-d.4.787

Cited by 6 publications

(5 citation statements)

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“…In our algorithm, the user does not need to provide precisely segmented regions, instead, the boundary checking algorithm are used to support segmented regions. For region-based image retrieval, we adopt the unsupervised texture segmentation method [30,33]. In [30], Ojala et al use the nonparametric log-likelihood-ratio test and the G statistic to compare the similarity of feature distributions.…”

Section: Image Segmentation and Region Representation 41 Image Segmmentioning

confidence: 99%

“…Based on this method, a boundary checking algorithm [34] has been proposed to improve the segmentation accuracy and computational cost. For more details about our segmentation algorithm, we refer the reader to [33]. In this Chapter, the weighted distribution of global information CIH (color index histogram) and local information LBP (local binary pattern) are applied to measure the similarity of two adjacent regions.…”

Section: Image Segmentation and Region Representation 41 Image Segmmentioning

confidence: 99%

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Content-Based Image Feature Description and Retrieving

Yang¹,

Kuo²,

Chang³

2013

Search Algorithms for Engineering Optimization

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Addi"ional informa"ion is available a" "he end of "he chap"er h""p://dx.doi.org/10.5772/45841 . IntroductionWith the growth in the number of color images, developing an efficient image retrieval system has received much attention in recent years. The first step to retrieve relevant information from image and video databases is the selection of appropriate feature representations e.g. color, texture, shape so that the feature attributes are both consistent in feature space and perceptually close to the user [ ]. There are many C"IR systems, which adopt different low level features and similarity measure, have been proposed in the literature [ -]. In general, perceptually similar images are not necessarily similar in terms of low-level features [ ]. Hence, these content-based systems capture pre-attentive similarity rather than semantic similarity [ ]. In order to achieve more efficient C"IR system, active researches are currently focused on the two complemented approaches region-based approach [ , -] and relevance feedback [ , -].Typically, the region-based approaches segment each image into several regions with homogenous visual prosperities, and enable users to rate the relevant regions for constructing a new query. In general, an incorrect segmentation may result in inaccurate representation. However, automatically extracting image objects is still a challengeing issue, especially for a database containing a collection of heterogeneous images. For example, Jing et al. [ ] integrate several effective relevance feedback algorithms into a region-based image retrieval system, which incorporates the properties of all the segmented regions to perform many-tomany relationships of regional similarity measure. However, some semantic information will be disregarded without considering similar regions in the same image. In another study [ ], Vu et al. proposed a region-of-interest ROI technique which is a sampling-based ap-© 2013 Yang e" al.; licensee InTech. This is an open access ar"icle dis"rib""ed "nder "he "erms of "he Crea"ive Commons A""rib""ion License (h""p://crea"ivecommons.org/licenses/by/3.0), which permi"s "nres"ric"ed "se, dis"rib""ion, and reprod"c"ion in any medi"m, provided "he original work is properly ci"ed.proach called SamMatch for matching framework. This method can prevent incorrectly detecting the visual features.On the other hand, the mechanism of relevance feedback is an online-learning technique that can capture the inherent subjectivity of user's perception during a retrieval session. In Power Tool [ ], the user is allowed to give the relevance scores to the best matched images, and the system adjusts the weights by putting more emphasis on the specific features. Cox et al. [ ] propose an alternative way to achieve C"IR that predicts the possible image targets by "ayes' rule rather than provides with segmented regions of the query image. However, the feedback information in [ ] could be ignored if the most likely images and irrelevant images have similar features.In this Chapter, a novel re...

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Section: Image Segmentation and Region Representation 41 Image Segmmentioning

confidence: 99%

Section: Image Segmentation and Region Representation 41 Image Segmmentioning

confidence: 99%

Content-Based Image Feature Description and Retrieving

Yang¹,

Kuo²,

Chang³

2013

Search Algorithms for Engineering Optimization

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“…The process of color segmentation consists of color representation, color feature extraction, similarity measurement and classification. In color representation, the RGB (Red, Green and Blue) model, which expresses color as a mixture of red, green and blue three color components, is often used to depict the color information of an image (Bascle et al, 2007;Weng et al, 2007). By using a transformation, the secondary colors, which are CMY (Cyan, Magenta and Yellow) or RG-GB-BR, can be obtained and used as an alternative color model (Wang et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Image-to-MIDI mapping based on dynamic fuzzy color segmentation for visually impaired people

Chen

Liao

Tai

2011

Pattern Recognition Letters

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“…Ilea and Whelan (2008) developed an unsupervised image segmentation method (referred to as CTex) that is based on the adaptive inclusion of colour and texture in the process of data partition. A burn colour image segmentation and classification system based on colour and texture feature information was proposed by Acha et al (2005) and Weng et al (2007) used weighted distributions of colour index histogram and LBP to measure the similarity of adjacent texture regions during the segmentation process. A multichannel segmentation algorithm is proposed which uses both grey‐level intensity and texture‐based features for region extraction.…”

Section: Introductionmentioning

confidence: 99%

A generic framework for colour texture segmentation

Nammalwar

Ghita²,

Whelan³

2010

Sensor Review

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PurposeThe purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.Design/methodology/approachThe framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.FindingsThe inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.Originality/valueThe novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

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Color Texture Segmentation Using Color Transform and Feature Distributions

Cited by 6 publications

References 0 publications

Content-Based Image Feature Description and Retrieving

Content-Based Image Feature Description and Retrieving

Image-to-MIDI mapping based on dynamic fuzzy color segmentation for visually impaired people

A generic framework for colour texture segmentation

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