Title :
Selection of Gabor filters for improved texture feature extraction
Author :
Li, Weitao ; Mao, Kezhi ; Zhang, Hong ; Chai, Tianyou
Author_Institution :
Res. Center of Autom., Northeastern Univ., Shenyang, China
Abstract :
Texture feature has been widely used in object recognition, image content analysis and many others. Among various approaches to texture feature extraction, Gabor filter has emerged as one of the most popular ones. Gabor filter-based feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smooth parameters of Gaussian envelope. In the literature, different parameter settings have been suggested, and filter banks created by these parameter settings work well in general. From the perspective of pattern classification, however, filter banks thus designed may not be ideal. In the present study, we propose a new approach to Gabor filter bank design, by incorporating feature selection, i.e. filter selection, into the design process. The merits of incorporating filter selection in filter bank design are twofold. Firstly, filter selection produces a compact Gabor filter bank and hence reduces computational complexity of texture feature extraction. Secondly, Gabor filter bank thus designed produces low-dimensional feature representation with improved sample-to-feature ratio, and this in turn leads to improved performance of texture classification. Experiment results on benchmark datasets and a real application have demonstrated the effectiveness of the proposed method.
Keywords :
Gabor filters; feature extraction; image texture; object recognition; Gabor filter; feature extraction; object recognition; pattern classification; texture classification; Accuracy; Feature extraction; Frequency modulation; Ores; Pattern recognition; Petroleum; Training; Fisher ratio measure; Gabor filter bank; Gabor filter selection; Texture feature;
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2010.5653278