Safe binary particle swam algorithm for an enhanced unsupervised label refinement in automatic face annotation

Chang, Jieh-Ren; Juang, Hung-Chi; Chen, You-Shyang; Chang, Che‐Chen

doi:10.1007/s11042-016-4058-y

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…When each particle searches for the optimal solution, it continuously marks itself as an individual extreme value, and shares the individual extreme value with other particles until the optimal individual extreme value is found [33]. The speed and position of the particles are adjusted by searching for individual extremums and global optimal solutions [34].…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network

et al. 2021

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In the intelligent manufacturing of furniture, the power signal has the characteristics of low cost and high accuracy and is often used as a tool wear condition monitoring signal. However, the power signal is not very sensitive to tool wear conditions. The present work addresses this issue by proposing a novel woodworking tool wear condition monitoring method that employs a limiting arithmetic average filtering method and particle swarm optimization (PSO)-back propagation (BP) neural network algorithm. The limiting arithmetic average filtering method was used to process the power signal and extracted the features of the woodworking tool wear conditions. The spindle speed, depths of milling, features and tool wear conditions were used as sample vectors. The PSO-BP neural network algorithm was used to establish the monitoring model of the woodworking tool wear condition. Experiments show that the proposed limiting arithmetic average filtering method and PSO-BP neural network algorithm can accurately monitor the woodworking tool wear conditions under different milling parameters.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network

et al. 2021

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“…Chang et al [17] described the use of an unsupervised label refinement (ULR) method for the task of fixing weakly labelled facial image data collected from the Internet with mislabelled images. To improve the correction accuracy of ULR, particle swarm optimization (PSO) and binary particle swarm optimization (BPSO) were used to solve the binary constraint optimization task in that study.…”

Section: Face Attribute Prediction and Annotationmentioning

confidence: 99%

A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices

Hachaj

2020

Sensors

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This paper proposes a classifier designed for human facial feature annotation, which is capable of running on relatively cheap, low power consumption autonomous microcomputer systems. An autonomous system is one that depends only on locally available hardware and software—for example, it does not use remote services available through the Internet. The proposed solution, which consists of a Histogram of Oriented Gradients (HOG) face detector and a set of neural networks, has comparable average accuracy and average true positive and true negative ratio to state-of-the-art deep neural network (DNN) architectures. However, contrary to DNNs, it is possible to easily implement the proposed method in a microcomputer with very limited RAM memory and without the use of additional coprocessors. The proposed method was trained and evaluated on a large 200,000 image face data set and compared with results obtained by other researchers. Further evaluation proves that it is possible to perform facial image attribute classification using the proposed algorithm on incoming video data captured by an RGB camera sensor of the microcomputer. The obtained results can be easily reproduced, as both the data set and source code can be downloaded. Developing and evaluating the proposed facial image annotation algorithm and its implementation, which is easily portable between various hardware and operating systems (virtually the same code works both on high-end PCs and microcomputers using the Windows and Linux platforms) and which is dedicated for low power consumption devices without coprocessors, is the main and novel contribution of this research.

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Safe binary particle swam algorithm for an enhanced unsupervised label refinement in automatic face annotation

Cited by 2 publications

References 30 publications

Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network

Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network

A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices

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