Performance analysis of Gabor wavelet for extracting most informative and efficient features

Gabor wavelet can extract most informative and efficient texture features for different computer vision and multimedia applications. Features extracted by Gabor wavelet have similar information as visualized by the receptive field of simple cells in the visual cortex of the mammalian brains. This motivates researchers to use Gabor wavelet for feature extraction. Gabor wavelet features are used for many multimedia applications such as stereo matching, face and facial expression recognition (FER), texture representation for segmentation. This motivates us to analyze Gabor features to evaluate their effectiveness in representing an image. In this paper, three major characteristics of Gabor features are established viz., (i) Real coefficients of Gabor wavelet alone is sufficient enough to represent an image; (ii) Local Gabor wavelet features with overlapping regions represent an image more accurately as compared to the global Gabor features and the local features extracted for the non-overlapping regions; and (iii) Real coefficients of overlapping regions are more robust to radiometric changes as compared to the features extracted from both global and local (non-overlapping regions) by using real, imaginary and magnitude information of a Gabor wavelet. The efficacy and effectiveness of these findings are evaluated by reconstructing the original image using the extracted features, and subsequently the reconstructed image is compared with the original image. Experimental results show that the local Gabor wavelet features extracted from overlapping regions represent an image more efficiently than the global and non-overlapping region-based features. Experimental results also show that the real coefficients alone is sufficient enough to represent an image more accurately as compared to the imaginary and magnitude informations.