Textured Image Segmentation

Abstract : The problem of image texture analysis is introduced, and existing approaches are surveyed. An empirical evaluation method is applied to two texture measurement systems, co-occurrence statistics and augmented correlation statistics. A spatial-statistical class of texture measures is then defined and evaluated. It leads to a simple class of texture energy transforms, which perform better than any of the preceding methods. These transforms are very fast, and can be made invariant to changes in luminance, contrast, and rotation without histogram equalization or other preprocessing. Texture energy is measured by filtering with small masks, typically 5x5, then with a moving-window average of the absolute image values. This method, similar to human visual processing, is appropriate for textures with short coherence length or correlation distance. The filter masks are integer-valued and separable, and can be implemented with one-dimensional or 3x3 convolutions. The averaging operation is also very fast, with computing time independent of window size. Texture energy planes may be linearly combined to form a smaller number of discriminant planes. These principal component planes seem to represent natural texture dimensions, and to be more reliable texture measures than the texture energy planes. Texture segmentation or classification may be accomplished using either texture energy or principal component planes as input. This study classified 15x15 blocks of eight natural textures. Accuracies of 72% were achieved with co- occurrence statistics, 65% with augmented correlation statistics, and 94% with texture energy statistics.