## 1 Introduction  
Texture [1; 2] is a core property of object appearance in natural scenes, ranging from large-scale samples to microscopic ones. It is also active visual information that is used to describe and recognize objects in the real environment. Texture classification is one of the key problems in texture analysis, and it has been a long-standing research topic because of its importance in understanding the process of texture classification by humans and its extensive applications in computer vision and image analysis [3]. 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. In general, 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]. However, 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 the discrimination of individual texture features, especially those with similar descriptions. However, a single filter is difficult to apply to multi-orientation and multiscale texture features. Thus, a bank of Gabor filters with the ability to extract multi-orientation and multiscale texture features was proposed to address the issue. Gabor filter has been extensively used for texture feature extraction and texture classification [13]. [PERSON] [14] captured the dependence of Gabor filter on different channels, and had better performance results than that of state-of-the-art approaches. [PERSON] [15] proposed a palm print recognition method that used a bank of Gabor filters to extract texture features from images, and the method achieved higher accuracy than texture classification approaches based on single Gabor filter. [PERSON] [16] proposed a new technique of identifying group-housed pigs on the basis of Gabor filter, and the experimental results demonstrate that the accuracy outperformed the compared approach by 91.86%, and PCA parameter drifted in the range of 0.85-0.9. [PERSON] [17] presented a texture classification method for fracture risk estimation based on Gabor filter combined with the total T-score, and the shape texture features could be measured in a wider area compared with other methods. [PERSON] [18] proposed a novel texture classification technique with directional statistical (DS)-Gabor filter and had satisfactory rotation invariance owing to the combination of a number of directional statistics. However, the parameters of the above method need to be set by experience, thus it may not be adaptable to different types of texture features. Furthermore, the parameter settings of each Gabor filter are crucial to classification accuracy. On the other hand, the process of obtaining the optimal parameters of Gabor filter can be seen as a combinatorial optimization problem that can be tackled by swarm intelligence algorithms. For example, [PERSON] [19; 20] presented a technique for the optimization of each filter individually via particle swarm optimization (PSO) and cuckoo search (CS) algorithm, and the approach successfully represents local texture changes at multiple scales and orientations from mammograms with an effective improvement in classification accuracy. [PERSON] [21] proposed a defect detection technique via Gabor filter to inspect flaws in woven fabrics in the fashion industry, and the differential evolution (DE) algorithm was utilized to obtain the optimal parameters of the filter bank. The method achieved high successful detection and low false alarm rates.