A Water-Area Recognition Approach Based on "Tuned" Texture Mask and Cuckoo Search Algorithm.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019;
2018:7690435. [PMID:
30627145 PMCID:
PMC6305020 DOI:
10.1155/2018/7690435]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 08/09/2018] [Accepted: 10/16/2018] [Indexed: 11/17/2022]
Abstract
Texture feature extraction is a key topic in many applications of image analysis; a lot of techniques have been proposed to measure the characteristics of this field. Among them, texture energy extracted with a mask is a rotation and scale invariant texture descriptor. However, the tuning process is computationally intensive and easily trap into the local optimum. In the proposed approach, a "Tuned" mask is utilized to extract water and nonwater texture; the optimal "Tuned" 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 feature extraction, the recognition accuracy is higher than the genetic algorithm (GA), particle swarm optimization (PSO) and the gravitational search algorithm (GSA) optimized "Tuned" mask scheme, and the water area could be well recognized from the original image. Experimental results show that the proposed method could exhibit better performance than other methods involved in the paper in terms of optimization ability and recognition result.
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