An Advanced Approach to Extraction of Colour Texture Features Based on GLCM

Benčo, Miroslav; Hudec, Róbert; Kamencay, Patrik; Zachariášová, Martina; Matúška, Slavomír

doi:10.5772/58692

Cited by 60 publications

(36 citation statements)

References 21 publications

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“…The GLCM is a robust image statistical analysis technique [27][28][29][30]. GLCM can be defined as a matrix of two dimensions of joint probabilities between pixels pairs, with a distance d between them in a given direction h [30].…”

Section: Grey-level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

An efficient similarity measure for content based image retrieval using memetic algorithm

Alsmadi

2017

Egyptian Journal of Basic and Applied Sciences

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Section: Grey-level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

An efficient similarity measure for content based image retrieval using memetic algorithm

Alsmadi

2017

Egyptian Journal of Basic and Applied Sciences

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“…Finally, statistical features are extracted from output histogram. A robust descriptor is proposed in (Benco et al, 2014), which is called color-level Co-occurrence matrixes (CLCM). First of all, graylevel co-occurrence matrixes (GLCM) and Gabor filters are used to extract texture features.…”

Section: Related Workmentioning

confidence: 99%

“…In order to report maximum accuracy, we uses reported results in (Benco et al, 2014) on Outex and Vistex datasets. The standard proposed approach in (Benco et al, 2014) is implemented on KTH-TIPS-2a. In Improved local binary patterns (ILBP) (Jin et al, 2004) the threshold used is the mean value of the whole neighborhood including the center pixel.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

“…It shows high mutability of MCSFS, which decreases it's usability in real applications. (Oajal et al, 2002) 95.83 96.04 69.8 CCTD (Khan et al, 2014) 98.20 95.52 82.7 PPU + SF (Fekriershad, 2011) 94.10 91.37 71.28 CLCM (Benco et al, 2014) 93.27 92.57 72.15 ILBP (Jin et al, 2004) 91.26 91.47 72.68 MBP (Hafiane et al, 2007) 92.37 93.59 74.62 CLBP_SP,R/MP,R/C( Zhenhua et al, 2010) 98.33 95.93 73.1 MCSFS (Porebski et al, 2013) 92.5 98.8 74.61 Wavelet Features (Xu et al, 2005) 85.2 86.43 63.83 1DLBP( Tajeripour and Fekri ershad, 2014) 97.9 94.9 79.75 LHS (Sharma et al, 2012) 97.32 93.26 73.0 NRLBP (Ren et al, 2013) 97.23 94.67 78.32 DMD (Mehta et al, 2016) 97…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

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Color texture classification based on proposed impulse-noise resistant color local binary patterns and significant points selection algorithm

Fekri-Ershad

Tajeripour

2017

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Purpose -The main aim of this paper is to propose a color-texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for our proposed approach. Design/methodology/approach -One of the efficient texture analysis operations is local binary patterns (LBP). The proposed approach includes two steps. First, a noise resistant version of color LBP is proposed to decrease it's sensitivity to noise. This step is evaluated based on combination of color sensor information using AND operation. In second step, a significant points selection (SPS) algorithm is proposed to select significant LBPs. This phase decreases final computational complexity along with increasing accuracy rate. Findings -The Proposed approach is evaluated using Vistex, Outex, and KTH-TIPS-2a data-sets. Our approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves highest accuracy. In two other experiments, result show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi-resolution, general usability are other advantages of our proposed approach. Originality/value -In the present paper, a new version of LBP is proposed originally, which is called Hybrid color local binary patterns (HCLBP). HCLBP can be used in many image processing applications to extract color/texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images. results show that using SPS algorithm as preprocess phase, increases classification accuracy. Noise resistant power of proposed HCLBP is compared with previous LBP versions. Low computational complexity, rotation invariant, multi-resolution, and low noise sensitivity are main advantages of our proposed approach. Also, HCLBP operation is a general method which can be used in many other applications to describe images. The proposed significant points selection algorithm can be used in many other image processing applications to choose key points. The proposed approach can be adapted with output images obtained using every kinds of digital cameras such as single-sensor or three-sensor cameras.

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“…In [12], the color feature and the ellipse shape feature are used for face location and recognition. In addition, the texture feature and the color feature are also used for image content retrieval and have achieved great results [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of insulator by combining k-nearest neighbor algorithm with multi-type feature for the Internet of Things

Zhu

Huang

et al. 2018

J Wireless Com Network

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New algorithms and architectures in the context of 5G shall be explored to ensure the efficiency, robustness, and consistency in variable application environments which concern different issues, such as the smart grid, water supply, gas monitoring, etc. In power line monitoring, we can get lots of images through a wide range of sensors and can ensure the safe operation of the smart grid by analyzing images. Feature extraction is critical to identify insulators in the aerial image. Existing approaches have primarily addressed this problem by using a single type of feature such as color feature, texture feature, or shape feature. However, a single type of feature usually leads to poor classification rates and missed detection in identifying insulators. Aiming to fully describe the characteristics of insulator and enhance the robustness of insulator against the complex background in aerial images, we combine three types of feature including color feature, texture feature, and shape feature towards a multi-type feature. Then, the multi-type feature is integrated with k-nearest neighbor classifier for automatic classifying insulators. Our experiment with 4500 aerial images demonstrates that the recognition rate is 99% by using this multi-type feature. Comparing to a single type of feature, our method yielded a better classification performance.

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An Advanced Approach to Extraction of Colour Texture Features Based on GLCM

Cited by 60 publications

References 21 publications

An efficient similarity measure for content based image retrieval using memetic algorithm

An efficient similarity measure for content based image retrieval using memetic algorithm

Color texture classification based on proposed impulse-noise resistant color local binary patterns and significant points selection algorithm

Automatic classification of insulator by combining k-nearest neighbor algorithm with multi-type feature for the Internet of Things

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