Ant Lion Optimizer for Texture Classification: A Moving Convolutional Mask - Mingwei Wang; Lizhe Wang; Zhiwei Ye; Juan Yang (IEEE Access, 2019)
Texture classification is an important issue for a number of applications in machine vision, which could be addressed through learning texture features by using a convolutional mask. However, the traditional convolutional mask has a single orientation, and the learning ability is limited. In addition, the optimization process often falls into the local optima, and the discrimination capacity of the learned mask is unsatisfactory. Thus, a novel moving convolutional mask is presented to enhance the discrimination of specific texture features in the proposed approach. Furthermore, how to achieve the satisfactory convolutional mask is considered as a combinatorial optimization problem and acquired by maximizing the texture energy by using ant lion optimizer (ALO). The proposed approach was tested on some public images, and the results were compared with those of the state-of-the-art approaches. The experimental results showed that ALO has strong optimization ability, and the proposed method is robust, adaptive and superior to the improved grey level co-occurrence matrix (GLCM), directional statistical Gabor filter and Tuned convolutional mask in terms of the fitness value and classification accuracy which has reached 27 and 91%, respectively, for all images.