A color image segmentation approach for content-based image retrieval
Pattern Recognition
Naked image detection based on adaptive and extensible skin color model
Pattern Recognition
Proceedings of the international workshop on Workshop on multimedia information retrieval
Application of wavelet decomposition and gradient variation in texture image retrieval
MUSP'08 Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing
Image Retrieval Using Modified Color Variation Co-occurrence Matrix
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Implementation of an image retrieval system using wavelet decomposition and gradient variation
WSEAS Transactions on Computers
A smart content-based image retrieval system based on color and texture feature
Image and Vision Computing
Optimal Discrete Wavelet Frames Features for Texture-Based Image Retrieval Applications
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
An effective image retrieval scheme using color, texture and shape features
Computer Standards & Interfaces
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MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
A fast image retrieval system based on color-space and color-texture features
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part V
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Pattern Recognition
A new content-based image retrieval technique using color and texture information
Computers and Electrical Engineering
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In this paper, we explore image retrieval mechanisms based on a combination of texture and color features. Texture features are extracted using Discrete Wavelet Frames (DWF) analysis, an over-complete decomposition in scale and orientation. Two-dimensional (2-D) or one-dimensional (1-D) histograms of the CIE Lab chromaticity coordinates are used as color features. The 1-D histograms of the a, b coordinates were modeled according to the generalized Gaussian distribution. The similarity measure defined on the feature distribution is based on the Bhattacharya distance. Retrieval benchmarking is performed over the Brodatz album and on images from natural scenes, obtained from the VisTex database of MIT Media Laboratory and from the Corel Photo Gallery. As a performance indicator recall (relative number of correct images retrieved) is measured on both texture and color separately and in combination. Experiments show this approach to be as effective as other methods while computationally more tractable.