Abstract
Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.
Highlights
Texture [1,2] is a core property of object appearance in natural scenes, ranging from large-scale samples to microscopic ones
To assess the quality of the proposed technique, public databases were utilized to extract features based on Gabor filter with several orientations and frequencies, as described
To evaluate the performance of the proposed texture classification method optimized by hybrid ant lion optimizer (HALO), three public texture databases were used in the experiment
Summary
Texture [1,2] is a core property of object appearance in natural scenes, ranging from large-scale samples to microscopic ones. The main applications of texture classification include understanding medical images, extracting visible objects, retrieving content-based images, inspecting industrial faults [4,5,6,7], and so on. The primary focus of studies on texture classification has been the determination of methods to extract texture features. It is generally believed that the extraction of powerful texture features is more important than that of weak texture features since they do not lead to good classification results, even when using excellent classifiers [8]. Texture features can be found in various orientations and at different scales, and these cannot be characterized effectively by commonly used methods [9,10,11,12]. Gabor filter has been used for this purpose and performs better in
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