A novel feature descriptor based coral image classification using extreme learning machine with ameliorated chimp optimization algorithm

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

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Remote Sensing Image Classification Algorithm Based on Texture Feature and Extreme Learning Machine

Computers Materials & Continua ◽

10.32604/cmc.2020.011308 ◽

2020 ◽

Vol 65 (2) ◽

pp. 1385-1395

Author(s):

Xiangchun Liu ◽

Jing Yu ◽

Wei Song ◽

Xiaobing Zhao ◽

Lizhi Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Image Classification ◽

Extreme Learning Machine ◽

Texture Feature ◽

Remote Sensing Image ◽

Classification Algorithm ◽

Remote Sensing Image Classification ◽

Learning Machine

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Label Rectification Learning through Kernel Extreme Learning Machine

Wireless Communications and Mobile Computing ◽

10.1155/2021/6669081 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Qiang Cai ◽

Fenghai Li ◽

Yifan Chen ◽

Haisheng Li ◽

Jian Cao ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Extreme Learning Machine ◽

Classification Performance ◽

Considerable Progress ◽

Strong Representation ◽

Kernel Extreme Learning Machine ◽

Classification Tasks ◽

Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

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