Accurate and Efficient Computation of Gabor Features in Real-Time Applications

Abstract

Gabor features are widely used in many computer vision applications such as image segmentation and pattern recognition. To extract Gabor features, a set of Gabor filters tuned to several different frequencies and orientations is utilized. The computational complexity of these features, due to their non-orthogonality, prevents their use in many real-time or near real-time tasks. Many research efforts have been made to address the computational complexity of Gabor filters. Most of these techniques utilize the separability of Gabor filters by decomposing them into 1-D Gaussian filter. The main issue in these techniques is the efficient pixel interpolation along the desired direction. Sophisticated interpolation mechanisms minimize the interpolation error with the increased computational complicity. This paper presents a novel framework in computation of Gabor features by utilizing a sophisticated interpolation scheme – quadratic spline – without increasing the overall computational complexity of the process. The main contribution of this work is the process of performing the interpolation and the convolution in a single operation. The proposed approach has been used successfully in real-time extraction of Gabor features from video sequence. The experimental results show that the proposed framework improves the accuracy of the Gabor features while reduces the computational complexity.