Designing compact Gabor filter banks for efficient texture feature extraction

Texture feature has been widely used in image segmentation, classification, retrieval and many others. Among various approaches to texture feature extraction, Gabor filtering has emerged as one of the most popular in recent years. Gabor filter-based texture feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smoothing parameters of the Gaussian envelope. In the literature, these parameters are often set by trial and error, based on the experience of the user, and the Gabor filter banks thus designed are often over-sized. To address the problem mentioned above, we propose to design compact Gabor filter banks by incorporating filter selection in this study. We develop a new Mahalanobis separability measure-based supervised approach to address the need of texture feature extraction. The strengths of our methods are twofold. Firstly, the proposed method provides a systematic way for Gabor filter bank design to avoid man-made bias. Secondly, the compact filter banks thus designed overcomes the problem of redundant or insignificant/irrelevant filter banks, and this in turn leads to improved performance of texture classification. Experimental results on benchmark datasets demonstrate the effectiveness of our proposed approach.