A Survey on Multimedia Ontologies for a Semantic Annotation of Cinematographic Resources for the Web of Data

Amaria, Samdalle; Kaladzavi, Guidedi; Lazarre, Warda; Kolyang,

doi:10.56578/ataiml010102

Cited by 4 publications

(4 citation statements)

References 22 publications

(25 reference statements)

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“…As a technology that segments images at the pixel level based on semantic understanding, it is positioned as a vanguard in the visionbased environmental perception arena [3]. Predominance of deep learning-oriented semantic segmentation approaches in this domain has been documented [4][5][6][7][8].…”

Section: Figure 1 Schematic Diagram Of Switchgear Intelligent Operati...mentioning

confidence: 99%

Semantic Segmentation Optimization in Power Systems: Enhancing Human-Like Switching Operations

Hua,

Zhao,

Zhang

et al. 2023

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In addressing the digital and intelligent transformation challenges within the traditional desulfurization process of coal secondary utilization, a perception learning model rooted in semantic segmentation networks has been developed. This model, when integrated with realworld operational environments, is shown to confront issues arising from symmetry and multi-scale features inherent to thermal power distribution room contexts. A combination of multi-scale feature fusion and attention mechanisms has facilitated the precise detection of human-like operation knobs on switch operation panels. To cope with extended dynamic scenes, a method relying on the visual bag-of-words has been adopted, wherein local image features are extracted and matched against a visual dictionary, resulting in a refined visual representation. The subsequent selection of consecutive symmetrically similar scene keyframes and the elimination of superfluous data have been observed to augment the efficacy of loop-closure detection. Such enhancements have culminated in improved accuracy in the SLAM (Simultaneous Localization and Mapping) of mobile robots, enabling their autonomous navigation to designated switching operation task locations. Experimental findings underscore the superiority of this optimized model over traditional semantic segmentation networks, with its ability to pinpoint operation knobs on electrical control cabinet panels in distribution rooms. Moreover, before initiating grasping actions under the Eye-in-hand architecture, visual servo grasping maneuvers can be executed, irrespective of the target's appearance angle within the field of view. This optimization offers an insightful foundation for potential integrations into patrol operation mobile robots, marking a feasible and effective stride forward.

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Section: Figure 1 Schematic Diagram Of Switchgear Intelligent Operati...mentioning

confidence: 99%

Semantic Segmentation Optimization in Power Systems: Enhancing Human-Like Switching Operations

Hua,

Zhao,

Zhang

et al. 2023

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“…Better decisions and promotions are possible, if the opinions and emotions can be extracted from these data. Text sentiment analysis through natural language processing (NLP) has become a hot topic at home and abroad [1][2][3]. Thanks to the in-depth development of informatization, network-based service provision can cut down the logistics management cost, and provide a convenient platform for the service objects to express their personal opinions [4].…”

Section: Introductionmentioning

confidence: 99%

Logistics Service Quality Sentiment Analysis with Deeper Attention LSTM Model with Aspect Embedding

2023

Teh. vjesn.

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To understand the audience's subjective perception of quality of service (QoS), it is important to analyze the data acquired from the logistics service logs and online evaluation system reasonably and effectively. Based on the analysis, rational improvement measures and decision suggestions can be developed to enhance the QoS. However, modern logistics service departments often face various business needs and service objects at the same time. If the evaluation subjects and their relationships are unclear in the service evaluation data, the sentiment analysis result of the text is a coarse-grained evaluation of the service as a whole. The lack of fine-grained pertinent evaluation results will hinder the improvement of specific management measures. To solve the problem, this paper designs an attention-based long short-term memory network (AT-LSTM) to divide the service reviews into ten topic relations, and then builds a deeper attention LSTM with aspect embedding (AE-DATT-LSTM). The weight-sharing bidirectional LSTM (BiLSTM) trains the topic word vectors and the text word vectors, and fuses the resulting topic features and text features. After the processing of the deep attention mechanism, the sentiment class of each evaluation topic is obtained by the classifier. Finally, several experiments were carried out on different public datasets. The results show that our approach surpasses the previous attention-based sentiment analysis models in accuracy and stability of service quality sentiment analysis. The introduction of topic features and deep attention mechanism is of great significance for the QoS-based sentiment classification b, and provides a feasible method for other fields like public opinion analysis, question answering system, and text reasoning.

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“…With the development of computer technology and the continuous improvement of CPU computing power, video image processing technology, which can realize video image enhancement and restoration, target recognition and positioning, is also developing rapidly [1][2][3][4]. Video image salient target detection is to simulate human visual perception system, intelligently detect salient targets in video images from semantic level, and finally realize independent analysis and understanding of video image content [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Salient Target Detection Method of Video Images Based on Convolution Neural Network

Yao¹,

Guo²

2022

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Traditional target detection of video images is often used to distinguish the relevant classification of large categories of targets, in the case of complex and diverse image content, it cannot capture enough visual cues, which makes it difficult to distinguish small differences between categories. Therefore, this article studies the salient target detection method of video images based on convolution neural network. Based on dictionary learning, the dynamic features of videos are extracted, and then the coefficient matrix is generated based on the dictionary to complete the learning, so as to realize the complete description of the underlying dynamics of videos. DMD algorithm is used to extract the dynamic mode of videos, and finally the foreground and background of video image frames are separated. Based on YOLOv4 network model, the salient target detection model of video images is constructed. Aiming at the defects of YOLOv4 network model, such as redundant parameters, many convolution modules and complex architecture, a series of model optimization are carried out. Experimental results verify the effectiveness of the model.

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A Survey on Multimedia Ontologies for a Semantic Annotation of Cinematographic Resources for the Web of Data

Cited by 4 publications

References 22 publications

Semantic Segmentation Optimization in Power Systems: Enhancing Human-Like Switching Operations

Semantic Segmentation Optimization in Power Systems: Enhancing Human-Like Switching Operations

Logistics Service Quality Sentiment Analysis with Deeper Attention LSTM Model with Aspect Embedding

Salient Target Detection Method of Video Images Based on Convolution Neural Network

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