Application of cuckoo search algorithm for texture recognition based on water areas

Peng, Kangbo; Chen, Zhongwei; Lai, Hong‐Jian; Wu, Xiaolong

doi:10.1117/12.2503078

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…In order to assess the property of the proposed texture classification technique, 5 public images with obvious texture features are utilized in this section. Some commonly used swarm intelligence algorithms, for instance PSO [19], HBMO [20], GSA [21] and CS algorithms [22] are used to make a comparison, and the computational complexity is O(nlnn) for all of the algorithms above [37]. Furthermore, some other texture classification techniques such as modified GLCM [5], rotated LBP [10], directional statistical Gabor [12] and ''Tuned'' convolutional mask [18] are also used to make a further comparison.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, some traditional swarm intelligence algorithms are also tested for comparison in this paper. Some existing swarm intelligence algorithm based texture classification approaches, which are proposed by Ye (PSO [19]), Zheng (HBMO [20]), Wan (GSA [21]) and Peng (CS [22]) are used to make a comparison. To make a fair comparison, the standard types of PSO, HBMO, GSA and CS algorithms are used, and the primary ALO is used here.…”

Section: A Parameter Settings For Swarm Intelligence Algorithmsmentioning

confidence: 99%

“…Wan et al introduced a texture classification by obtaining a convolutional mask using GSA, which was exemplified in a residential area and the performance is better than some commonly used techniques [21]. Peng et al utilized CS algorithm, which had better optimization ability and could produce better mask to learn a convolutional mask and extracted the water area from an image [22]. In short, PSO, HBMO, GSA and CS algorithms can obtain good convolutional masks.…”

Section: Introductionmentioning

confidence: 99%

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Ant Lion Optimizer for Texture Classification: A Moving Convolutional Mask

Wang

et al. 2019

IEEE Access

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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. INDEX TERMS Moving convolutional mask, texture classification, feature extraction, ant lion optimizer.

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Section: Resultsmentioning

confidence: 99%

Section: A Parameter Settings For Swarm Intelligence Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ant Lion Optimizer for Texture Classification: A Moving Convolutional Mask

Wang

et al. 2019

IEEE Access

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“…The cuckoo search (CS) algorithm is very often used for optimization without constraints [16,17]. A much smaller number of manuscripts have addressed the application of the CS algorithm when solving constrained optimization problems [18,19].…”

Section: Introductionmentioning

confidence: 99%

Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

2021

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This paper presents the application of the cuckoo search (CS) algorithm in attempts to the minimization of the commutation torque ripple in the brushless DC motor (BLDC). The optimization algorithm was created based on the cuckoo’s reproductive behavior. The lumped-parameters mathematical model of the BLDC motor was developed. The values of self-inductances, mutual inductances, and back-electromotive force waveforms applied in the mathematical model were calculated by the use of the finite element method. The optimization algorithm was developed in Python 3.8. The CS algorithm was coupled with the static penalty function. During the optimization process, the shape of the voltage supplying the stator windings was determined to minimize the commutation torque ripple. Selected results of computer simulation are presented and discussed.

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