Texture analysis and classification using deterministic tourist walk

Backes, André Ricardo; Gonçalves, Wesley Nunes; Martinêz, Alexandre Souto; Bruno, Odemir Martinez

doi:10.1016/j.patcog.2009.07.017

Cited by 110 publications

(99 citation statements)

References 37 publications

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“…3, experiments are described and performed on standard colored texture images. These experiment show that the presented algorithm outperform traditional methods (Chromatic moments [22], Color differential [23] and to the gray-scale deterministic walker previous version [1]). We address our final remarks in Sec.…”

Section: Introductionmentioning

confidence: 71%

“…[1], the partially self-avoinding deterministic walk has been proposed as a tools to texture analysis and classification. This algorithm has been developed to analyze gray level texture.…”

Section: Walks On Color Images and Texture Signaturementioning

confidence: 99%

“…From the study of these distributions, using statistical techniques, it is possible to achieve a signature able to discriminate the image texture [1,13,14,17].…”

Section: Walks On Color Images and Texture Signaturementioning

confidence: 99%

“…Recently, we have proposed a novel approach of texture analysis that achieved better results than most of the state-of-art methods [1,13,14]. It considers independent walkers leaving from each pixel of an image.…”

Section: Introductionmentioning

confidence: 99%

“…In Sec. 2, we review how this walk can be used to analyse and classify textures in images [1] and the (t, p)-joint distributions are used to generate a single signature vector for colored images. In Sec.…”

Section: Introductionmentioning

confidence: 99%

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Color Texture Analysis and Classification: An Agent Approach Based on Partially Self-avoiding Deterministic Walks

Backes

Martinêz

Bruno

2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Self Cite

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Abstract. Recently, we have proposed a novel approach of texture analysis that has overcome most of the state-of-art methods. This method considers independent walkers, with a given memory, leaving from each pixel of an image. Each walker moves to one of its neighboring pixels according to the difference of intensity between these pixels, avoiding returning to recent visited pixels. Each generated trajectory, after a transient time, ends in a cycle of pixels (attractor) from where the walker cannot escape. The transient time (t) and cycle period (p) form a joint probability distribution, which contains image pixel organization characteristics. Here, we have generalized the texture based on the deterministic partially self avoiding walk to analyze and classify colored textures. The proposed method is confronted with other methods, and we show that it overcomes them in color texture classification.

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Section: Introductionmentioning

confidence: 71%

“…[1], the partially self-avoinding deterministic walk has been proposed as a tools to texture analysis and classification. This algorithm has been developed to analyze gray level texture.…”

Section: Walks On Color Images and Texture Signaturementioning

confidence: 99%

“…From the study of these distributions, using statistical techniques, it is possible to achieve a signature able to discriminate the image texture [1,13,14,17].…”

Section: Walks On Color Images and Texture Signaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Color Texture Analysis and Classification: An Agent Approach Based on Partially Self-avoiding Deterministic Walks

Backes

Martinêz

Bruno

2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Shortest Paths in HSI Space for Color Texture Classification

Jin

Dong

Zheng

et al. 2018

Intelligence Science II

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Color texture representation is an important step in the task of texture classification. Shortest paths was used to extract color texture features from RGB and HSV color spaces. In this paper, we propose to use shortest paths in the HSI space to build a texture representation for classification. In particular, two undirected graphs are used to model the H channel and the S and I channels respectively in order to represent a color texture image. Moreover, the shortest paths is constructed by using four pairs of pixels according to different scales and directions of the texture image. Experimental results on colored Brodatz and USPTex databases reveal that our proposed method is effective, and the highest classification accuracy rate is 96.93% in the Brodatz database.

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Deterministic Partially Self-avoiding Walks on Networks for Natural Shapes Classification

Ribas

Bruno

2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Texture analysis and classification using deterministic tourist walk

Cited by 110 publications

References 37 publications

Color Texture Analysis and Classification: An Agent Approach Based on Partially Self-avoiding Deterministic Walks

Color Texture Analysis and Classification: An Agent Approach Based on Partially Self-avoiding Deterministic Walks

Shortest Paths in HSI Space for Color Texture Classification

Deterministic Partially Self-avoiding Walks on Networks for Natural Shapes Classification

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