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2007
IEEE

Iterative Feature Selection for Color Texture Classification

11 years 1 months ago
Iterative Feature Selection for Color Texture Classification
In this paper, we describe a new approach for color texture classification by use of Haralick features extracted from color co-occurrence matrices. As the color of each pixel can be represented in different color spaces, we automatically determine in which color spaces, these features are most discriminating for the textures. The originality of this approach is to select the most discriminating color texture features in order to build a feature space with a low dimension. Our method, based on a supervised learning scheme, uses an iterative selection procedure. It has been applied and tested on the BarkTex benchmark database.
Alice Porebski, Nicolas Vandenbroucke, Ludovic Mac
Added 21 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2007
Where ICIP
Authors Alice Porebski, Nicolas Vandenbroucke, Ludovic Macaire
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