Journal ArticleDOI
Image retrieval based on the texton co-occurrence matrix
Guang-Hai Liu,Jingyu Yang +1 more
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TLDR
Zhang et al. as mentioned in this paper put forward a new method of co-occurrence matrix to describe image features, which can express the spatial correlation of textons, and quantized the original images into 256 colors and computed color gradient from the RGB vector space.About:
This article is published in Pattern Recognition.The article was published on 2008-12-01. It has received 163 citations till now. The article focuses on the topics: Image texture & Color histogram.read more
Citations
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Journal ArticleDOI
Content-based image retrieval using color difference histogram
Guang-Hai Liu,Jingyu Yang +1 more
TL;DR: Experimental results demonstrate that the novel color difference histograms (CDH) method is much more efficient than the existing image feature descriptors that were originally developed for content-based image retrieval, such as MPEG-7 edge histogram descriptors, color autocorrelograms and multi-texton histograms.
Journal ArticleDOI
Image retrieval based on micro-structure descriptor
TL;DR: The results demonstrate that the proposed micro-structure descriptor is much more efficient and effective than representative feature descriptors, such as Gabor features and multi-textons histogram, for image retrieval.
Journal ArticleDOI
Image retrieval based on multi-texton histogram
TL;DR: The results demonstrate that it is much more efficient than representative image feature descriptors, such as the edge orientation auto-correlogram and the texton co-occurrence matrix and has good discrimination power of color, texture and shape features.
Journal ArticleDOI
Content-based image retrieval using computational visual attention model
TL;DR: This paper proposes a novel computational visual attention model, namely saliency structure model, for content-based image retrieval, and introduces a novel visual cue, namely color volume, with edge information together, to detect saliency regions.
Journal ArticleDOI
A novel method for image retrieval based on structure elements' descriptor
Xingyuan Wang,Zongyu Wang +1 more
TL;DR: Zhang et al. as mentioned in this paper proposed structure elements' descriptor (SED), a novel texture descriptor, which can effectively describe images and represent image local features and extract and describe color and texture features.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Book
Image Processing: Analysis and Machine Vision
TL;DR: The digitized image and its properties are studied, including shape representation and description, and linear discrete image transforms, and texture analysis.
Journal ArticleDOI
Texture features for browsing and retrieval of image data
B.S. Manjunath,Wei-Ying Ma +1 more
TL;DR: Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy.
Proceedings ArticleDOI
Image indexing using color correlograms
TL;DR: Experimental evidence suggests that this new image feature called the color correlogram outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.
Journal ArticleDOI
Textons, the elements of texture perception, and their interactions
TL;DR: Research with texture pairs having identical second-order statistics has revealed that the pre-attentive texture discrimination system cannot globally process third- and higher- order statistics, and that discrimination is the result of a few local conspicuous features, called textons.