Image retrieval based on the texton co-occurrence matrix

Liu, Guanghai; Yang, Jingyu

doi:10.1016/j.patcog.2008.06.010

Cited by 171 publications

(94 citation statements)

References 11 publications

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“…In this section, We briefly review, texton cooccurrence matrix (TCM) [1] and Multi texton histogram (MTH) [2] methods.…”

Section: Brief Description About Tcm and Mthmentioning

confidence: 99%

“…The goal of textons is to describe complex structures with simpler primitives. The texton co-occurrence matrices (TCM) [1] and multi-texton histogram (MTH) [2] can describe the spatial correlation of textons, for an image retrieval. This paper proposes an efficient mechanism for generation of texton features and extraction of low level features from textons.…”

Section: Introductionmentioning

confidence: 99%

“…The present descriptor can be viewed as an improved version of texton cooccurrence matrix (TCM) [1] and Multi-texton histogram (MTH) [2]. It is specially designed for natural image analysis and can achieve higher classification rate.…”

mentioning

confidence: 99%

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Texture Classification Using Complete Texton Matrix

Kumari¹,

Kumar²,

Satyanarayana³

2017

IJIGSP

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Abstract-This paper presents a complete image feature representation, based on texton theory proposed by Julesz's, called as a complete texton matrix (CTM)for texture image classification. The present descriptor can be viewed as an improved version of texton cooccurrence matrix (TCM) [1] and Multi-texton histogram (MTH) [2]. It is specially designed for natural image analysis and can achieve higher classification rate. TheCTM can express the spatial correlation of textons and can be considered as a generalized visual attribute descriptor. This paper initially quantized the original textures into 256 colors and computed color gradient from RGB vector space. Then the statistical information of eleven derived textons, on a 2 x 2 grid in a nonoverlapped manner are computed to describe image features more precisely. To reduce the dimensionality the present paper extended the concept of present descriptor and derived a compact CTM (CCTM). The proposed CTM and CCTM methods are extensively tested on the Brodtaz, Outex and UIUC natural images. The results demonstrate the superiority of the present descriptor over the state-of-art representative schemes such as uniform LBP (ULBP), local ternary pattern (LTP), complete -LBP (CLBP), TCM and MTH.

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“…In this section, We briefly review, texton cooccurrence matrix (TCM) [1] and Multi texton histogram (MTH) [2] methods.…”

Section: Brief Description About Tcm and Mthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Texture Classification Using Complete Texton Matrix

Kumari¹,

Kumar²,

Satyanarayana³

2017

IJIGSP

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“…There are some algorithms that combine color and texture features together, such as the integrative co-occurrence matrix [12], texton co-occurrences matrix [13], multi-texton histogram (MTH) [4], color edge co-occurrence histogram (CECH) [14], color auto-correlograms [5], etc. Although computing Gabor features separately for each channel can be used as a colortexture descriptor, the computational burden of Gabor filtering is relatively big.…”

Section: Related Workmentioning

confidence: 99%

Image retrieval based on micro-structure descriptor

Liu

Zhang

et al. 2011

Pattern Recognition

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299

203

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a b s t r a c tThis paper presents a simple yet efficient image retrieval approach by proposing a new image feature detector and descriptor, namely the micro-structure descriptor (MSD). The micro-structures are defined based on an edge orientation similarity, and the MSD is built based on the underlying colors in microstructures with similar edge orientation. With micro-structures serving as a bridge, the MSD extracts features by simulating human early visual processing and it effectively integrates color, texture, shape and color layout information as a whole for image retrieval. The proposed MSD algorithm has high indexing performance and low dimensionality. Specifically, it has only 72 dimensions for full color images, and hence it is very efficient for image retrieval. The proposed method is extensively tested on Corel datasets with 15,000 natural images. The results demonstrate that it is much more efficient and effective than representative feature descriptors, such as Gabor features and multi-textons histogram, for image retrieval.Crown

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“…In our prior works, they are also used for evaluating the performance of CBIR [16][17] [20][21][22]. They are defined as follows:…”

Section: A Performance Metricsmentioning

confidence: 99%

Content-based Image Retrieval Based On Visual Attention And The Conditional Probability

Liu¹

2015

Proceedings of the International Conference on Chemical, Material and Food Engineering

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Abstract-a novel content-based image retrieval framework was presented in this paper. This framework is used to encode primary visual feature and saliency information as natural image features by simulating visual attention mechanism and using the conditional probability. In this framework, the color volume is used as a novel feature to detect saliency areas. Besides, a novel generalized visual feature representation method, namely the conditional probability histogram, is proposed to describe natural image features. It can integrate primary visual features and saliency information into one whole unit. Experimental results indicate that the proposed algorithm outperform our prior works, namely multi-texton histogram and color difference histogram.

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Image retrieval based on the texton co-occurrence matrix

Cited by 171 publications

References 11 publications

Texture Classification Using Complete Texton Matrix

Texture Classification Using Complete Texton Matrix

Image retrieval based on micro-structure descriptor

Content-based Image Retrieval Based On Visual Attention And The Conditional Probability

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