Application of cuckoo search algorithm for texture recognition based on water areas
Kangbo Peng, Huang Lai, Zhongwei Chen, Xiaozhong Wu · 2018
Texture recognition is a key topic in many applications of image analysis; many techniques have been proposed to measure the characteristics of this field. Among them, texture energy extracted with the “Tuned” mask is a rotation and scale invariant texture descriptor. However, the tuning process is computationally intensive and easily to trap into local optimum. In the proposed approach, how to obtain the “Tuned” mask is viewed as a combinatorial optimization problem and the optimal mask is acquired by maximizing the texture energy value via a newly proposed cuckoo search (CS) algorithm. Experimental results on samples and images show that the proposed method is suitable for texture recognition, the recognition accuracy is higher than genetic algorithm (GA) and particle swarm optimization (PSO) optimized “Tuned” mask scheme, and the water areas can be well recognized from the original image. It is a robust and efficient method to obtain the optimal “Tuned” mask for texture analysis.