The present paper proposes a method of texture classification based on long linear patterns using wavelets. Linear patterns of long size are bright features defined by morphological properties: linearity, connectivity, width and by a specific Gaussian-like profile whose curvature varies smoothly along the crest line. The most significant information of a texture often appears in the occurrence of grain components. That’s why the present paper used sum of occurrence of grain components for feature extraction. The features are constructed from the different combinations of long linear patterns with different orientations. These features offer a better discriminating strategy for texture classification. Further, the distance function captured from the sum of occurrence of grain components of texture’s, is expected to enhance the class seperability power. The class seperability power of these features is investigated in the classification experiments with arbitrarily chosen texture images taken from the Brodatz album. The experimental results using different wavelet transforms indicates good analysis, and how the classification of textures will be effected with different long linear patterns.

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