Duplicate Image Representation Based on Semi-Supervised Learning

Chen, Ming; Yan, Jinghua; Gao, Tieliang; Li, Yuhua; Hanson, Mark A.

doi:10.4018/ijghpc.301578

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…In order to solve the relationship between a large number of images and classes, they built an image generation framework through semi-supervised learning to improve the efciency of automatic retrieval. Chen et al proposed a semi-supervised learning-based representation algorithm for repeated images when detecting image repeatability [38]. Teir proposed method guarantees the description and semantic similarity and achieves excellent performance in image retrieval.…”

Section: Semi-supervisedmentioning

confidence: 99%

Car Emotion Labeling Based on Color‐SSL Semi‐Supervised Learning Algorithm by Color Augmentation

Guo

Liu

et al. 2023

International Journal of Intelligent Systems

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In the era of emotional consumption, it has become a hot topic that commodities meet consumers’ emotional needs. As a necessity of life, the car also needs to meet the needs of consumers. To achieve that consumers can purchase cars according to their emotional needs, we need to label cars with emotional words. The car’s appearance is the crucial medium of emotional information transmission, especially the car’s color is an essential emotional factor. As the first impression of products, color affects people’s emotional attitude. Therefore, introducing color features into the training process of sample marking is an excellent idea for intelligent labeling of a large number of product emotions. This paper proposes a semi-supervised learning method, Color-SSL, based on color data augmentation to realize the label of car emotion. Color-SSL takes FlexMatch as the framework of a semi-supervised learning model and augments data by extracting subject color. Compared with the baseline method, the accuracy of this method improved by 3.2%, 8.3%, 8.6%, and 1.4% with 10, 50, 100, and 200 training samples and 1000 test samples. The results show that Color-SSL obtains the best emotion-label result (94%). In addition, this study publishes pictures of emotional car datasets with high resolution, orthogonal perspective, and uniform background.

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Section: Semi-supervisedmentioning

confidence: 99%

Car Emotion Labeling Based on Color‐SSL Semi‐Supervised Learning Algorithm by Color Augmentation

Guo

Liu

et al. 2023

International Journal of Intelligent Systems

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Deep learning in food category recognition

Zhang,

Deng,

Zhu

et al. 2023

Information Fusion

209

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Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

Cheng,

Yu,

Zhang

2024

Algorithms

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The recent advancement in wireless power transmission (WPT) has led to the development of wireless rechargeable sensor networks (WRSNs), since this technology provides a means to replenish sensor nodes wirelessly, offering a solution to the energy challenges faced by WSNs. Most of the recent previous work has focused on charging sensor nodes using wireless charging vehicles (WCVs) equipped with high-capacity batteries and WPT devices. In these schemes, a vehicle can move close to a sensor node and wirelessly charge it without physical contact. While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges of applied WCVs: off-road navigation and vehicle speed limitations. To overcome these challenges, previous work proposed a new WRSN model equipped with one drone coupled with several pads deployed to charge the drone when it cannot reach the subsequent stop. This wireless charging pad deployment aims to deploy the minimum number of pads so that at least one feasible routing path from the base station can be established for the drone to reach every SN in a given WRSN. The major weakness of previous studies is that they only consider deploying a wireless charging pad at the locations of the wireless sensor nodes. Their schemes are limited and constrained because usually every point in the deployed area can be considered to deploy a pad. Moreover, the deployed pads suggested by these schemes may not be able to meet the connected requirements due to sparse environments. In this work, we introduce a new scheme that utilizes the Quad-Tree concept to address the wireless charging pad deployment problem and reduce the number of deployed pads at the same time. Extensive simulations were conducted to illustrate the merits of the proposed schemes by comparing them with different previous schemes on maps of varying sizes. In the case of large maps, the proposed schemes surpassed all previous works, indicating that our approach is more suitable for large-scale network environments.

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Duplicate Image Representation Based on Semi-Supervised Learning

Cited by 3 publications

References 25 publications

Car Emotion Labeling Based on Color‐SSL Semi‐Supervised Learning Algorithm by Color Augmentation

Car Emotion Labeling Based on Color‐SSL Semi‐Supervised Learning Algorithm by Color Augmentation

Deep learning in food category recognition

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

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