Abstract
These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.
Highlights
Texture classification is an active research topic in image processing and computer vision
Porebski et al propose a different approach which selects, out of the nine Local Binary Pattern (LBP) histograms extracted from a color texture, those which are the most discriminant [27]
We propose to extend these scores in order to rank and select LBP histograms extracted from a color image
Summary
Texture classification is an active research topic in image processing and computer vision. It has received significant attention in many applications such as content based image retrieval, medical image analysis, face recognition, or biometrics. The texture classification approaches can typically be categorized into two subproblems [1,2]: The representation, which aims to characterize an image with a set of texture features, and the decision, which assigns this image to one of the available texture classes. This paper focuses on the first subproblem and on feature space dimensionality reduction techniques. Many approaches perform a reduction of the feature space to transform high-dimensional data into a meaningful representation of reduced dimensionality [3,4,5]. By only retaining the most discriminant features, these approaches aim to improve the classification accuracy, while decreasing the processing time
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