TEXTURE ALGORITHMS: PERFORMANCE VARIABILITY ACROSS DATA SETS

Texture analysis plays a vital role in the area of image understanding research. One of the key areas of research is to compare how well these algorithms rank in their ability to differentiate between different textures. Traditionally, texture algorithms have been applied mostly to benchmark data such as the Brodatz album and the studies have found that certain algorithms are better suited for differentiating between certain types of textures. It would be presumptuous to believe that these chosen algorithms will perform well on other types of image data. We investigate the variability in performance across different benchmark data sets. Four benchmarks are chosen for analysis, including Brodatz album, MeasTex, VisTex, and Minerva. We take a total of seven texture feature extraction methods and compare the algorithms on the basis of their rankings on texture classification recognition rate and draw some important conclusions.

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