Optimum Gabor filter design and local binary patterns for texture segmentation

Abstract

We present a novel approach to multi-texture image segmentation based on the formation of an effective texture feature vector. Texture sub-features are derived from the output of an optimized Gabor filter. The filter's parameters are selected by an immune genetic algorithm, which aims at maximizing the discrimination between the multi-textured regions. Next the texture features are integrated with a local binary pattern, to form an effective texture descriptor with low computational cost, which overcomes the weakness of the single frequency output component of the filter. Finally, a K-nearest neighbor classifier is used to effect the multi-texture segmentation. The integration of the optimum Gabor filter and local binary pattern methods provide a novel solution to the task. Experimental results demonstrate the effectiveness of the proposed approach.

Color and Local Maximum Edge Patterns Histogram for Content Based Image Retrieval

“…Success in terms of speed (no need to tune any parameters) and performance is reported in many research areas such as texture classification [17][18][19], face recognition [24][25][26], object tracking [30], biomedical image retrieval [31] and finger print recognition.…”

“…Heikkila et al used the LBP for interest region description [24]. Li et al used the combination of Gabor filter and LBP for texture segmentation [25]. Zhang et al proposed the local derivative pattern for face recognition [26].…”

Diagnosis of Induction Motor Faults via Gabor Analysis of the Current in Transient Regime

“…Gabor analysis has been extensively applied in different fields such as geophysics [46], pattern recognition [47], biomedical engineering [48], communications systems [49], image processing [50], [51], quality control [52], optics [53], power system analysis [54], [55], DNA classification [56], modal analysis [57], order tracking [58], etc. Gabor functions are the basis of the Morlet wavelet, which is widely used in machine condition monitoring [12], [16], [20].…”

Strip Steel Defect Detection Based on Saliency Map Construction Using Gaussian Pyramid Decomposition

“…1,2) At present, strip steel inspection in iron and steel mills is still performed manually by human inspectors, a process which suffers from both low efficiency and high labor intensity and cannot meet the requirement of online detection. 3,4) To increase the efficiency of strip steel defect detection, it is necessary to replace human inspectors with an automation detection system using computer vision. [5][6][7][8] In past decades, a lot of detection algorithms have been carried out by many researchers.…”