Image Texture Classification Using Combined Grey Level Co-Occurrence Probabilities and Support Vector Machines

Khoo, Hee-Kooi; Ong, Hong-Choon; Wong, Ya-Ping

doi:10.1109/cgiv.2008.47

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Khoo et al [6] model textures as GLCM and use a support vector machine (SVM) to classify textures. They evaluate their segmentation approach on few synthetic texture mosaics and two satellite images with good results.…”

Section: Related Workmentioning

confidence: 99%

Automated petroglyph image segmentation with interactive classifier fusion

Seidl

Breiteneder

2012

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

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The number of high quality images of rock panels containing petroglyphs grows steadily. Different time-consuming manual methods to determine and document the exact shapes and spatial locations of petroglyphs on a panel have been carried out over decades. The first step for classification and retrieval of petroglyphs is the segmentation of the images. In this paper, we present and evaluate an automated approach to segment petroglyph images.

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Section: Related Workmentioning

confidence: 99%

Automated petroglyph image segmentation with interactive classifier fusion

Seidl

Breiteneder

2012

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

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“…m. SUPPORT VECTOR MACHINES ALGORITHM (SVMs) SVM is a useful technique for data classification based on statistical learning theory. The purpose of SVM is to map from feature vectors into a higher dimensional feature space and then creating a separating hyperplane with maximum margin to classifY the features [12]. Higher dimensional space is defined by a kernel function.…”

Section: Gaussian Markov Random Fields (Gmrfs)mentioning

confidence: 99%

The texture classification using the fusion of decisions from different texture classifiers

Pharsook

Kasetkasem

Larmsrichan

et al. 2011

The 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thai

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The improved of texture classification accuracy by using the probability weighted combination method of three texture features extraction consist of thE0020 Gray-Level Co occurrence Matrix (GLCM), Semivariogram Function and Gaussian Markov Random Fields (GMRFs). Five different textures images are used in the experiment. The classifier that use for classify the extracted features in this research is Support Vector Machines (SVMs). The experimental result shows that the average accuracy of the combination method with probability weight up to 95.71 %, which is better than the simple combination method about 2% Keyword� Gray-Level Co-occurrence Matrix (GLCM), Gaussian Markov Random Fields (GMRFs), Semivarigram, Texture classification, Combination.

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“…Some of the popular kernels are shown in Schölkopf & Smola (2002). A combined GLCP and SVM technique has been proposed for two class segmentation with significant result in Khoo et al (2008).…”

Section: Feature Selectionmentioning

confidence: 99%