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.

The segmentation of colored texture images is considered. Either luminance, color, and/or texture features could be used for segmentation. For luminance and color the classes are described using the corresponding empirical probability distributions. The discrete wavelet frames analysis is used for obtaining features of texture patterns. At a first stage, pattern analysis is performed for extracting the features using the Bhattacharya distance. Two labeling algorithms are proposed. A deterministic relaxation algorithm using a likelihood based distance yields the labeling of pixels to the different color-texture patterns. In addition, a multi-label fast marching level set algorithm is utilized for the determination of the segment boundaries.