Fusion of Gabor filter and co-occurrence probability features for texture recognition

This paper explores a design-based method to fuse Gabor filter features and co-occurrence probability features for improved texture recognition. The fused feature set utilizes both the Gabor filter’s capability of accurately capturing lower frequency texture information and the co-occurrence probability’s capability in texture information relevant to higher frequency components. Fisher linear discriminant analysis indicates that the fused features have much higher feature space separation than the pure features. Image texture segmentation results are presented that also demonstrate the improvement using the fused feature sets.