Comparison of texture features based on Gabor filters.

Area of Science:

• Computer Vision
• Image Analysis
• Texture Analysis

Background:

• Texture analysis is crucial for image understanding.
• Gabor filters are widely used for texture feature extraction.
• Different post-processing methods impact feature performance.

Purpose of the Study:

• To compare texture features based on Gabor filter responses.
• To evaluate feature performance using Fisher's criterion and classification.
• To assess robustness against non-texture elements.

Main Methods:

• Utilized a bank of Gabor filters to extract local power spectra.
• Applied nonlinear post-processing: Gabor energy, complex moments, grating cell operator.
• Evaluated feature distinctiveness using Fisher's criterion and classification accuracy.

Main Results:

• The grating cell operator demonstrated superior texture discrimination and segmentation.
• Both Fisher's criterion and classification confirmed consistent performance rankings.
• The grating cell operator showed robustness, avoiding false responses to object contours.

Conclusions:

• The grating cell operator offers the best performance for texture analysis among the compared methods.
• This operator provides reliable texture detection and segmentation.
• Its selectivity makes it robust to non-texture image components.