Neighborhood and Haralick feature extraction for color texture analysis
First, we measure the discriminating power of Haralick features extracted from the color co-occurrence matrices of color images coded in 28 different color spaces, and we select the most discriminating one for different 3x3 neighborhoods. Then, we experimentally verify that the most discriminating feature space, built by using an iterative selection procedure, depends on the chosen neighborhood and finally we study the impact of the neighborhood choice on the classification results by using the same feature space but different neighborhoods.
Experimental results achieved with the Barktek database have firstly shown the adequacy between the discriminating power of the selected feature space and the rate of well-classified images. We have also seen that the choice of the neighborhood does not highly influence the selection of the most discriminating feature but has a significant impact on the quality of discrimination between the considered textures. Indeed, we have worked with textures which contain vertical patterns and have shown that the best classification results have been obtained with horizontal neighborhoods. The choice of the neighborhood depends consequently on the analysed textures.
Document Type: Research Article
Publication date: January 1, 2008
Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.
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