Video Affective Content Recognition Based on Film Grammars and Fuzzy Evaluation

Lin, Xianghong; Wen, Xiangming; Lu, Zhaoming

doi:10.1109/mmit.2008.87

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Professor Wang from University of Science and Technology Beijing and his team put forward modeling method based on emotion space, in which emotion is divided into several basic types, and the basic types are combined to form emotional space and through the calculation of the emotional entropy to obtain the conversion probability of emotion in different state [15]. Lin et al of Beijing University of Posts and Telecommunications made a deep study on the analysis of video emotional semantic, proposed an analysis algorithm of "emotional syllogism" and applied it to the emotional analysis of film video, and achieved good results [16,17].…”

Section: Related Researchesmentioning

confidence: 99%

Emotional Semantic Recognition of Visual Scene in Flash Animation

Shi

Xiang-zeng

2018

Journal of Control Science and Engineering

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Based on the organization structure of the Flash animation files, we first use the edge density method to segment the Flash animation to obtain the visual scenes, then extract the visual features such as color and texture as the input parameters of BP neural network, and set up the sample database. Secondly, we choose a suitable model for emotion classification, use eight kinds of emotional adjectives to describe the emotion of Flash animation, such as warm, delightful, exaggerated, funny, desolate, dreary, complex, and illusory, and mark the emotion value of the visual scene in the sample database and so use it as the output parameter of the BP neural network. Finally, we use BP neural network with appropriate transfer function and learning function for training to obtain the rules for mapping from visual features of the visual scene to semantic space and, at last, complete the automatic classification work of emotional semantic of the visual scene. We used the algorithm to carry on the emotional semantics recognition to 5012 visual scenes, and the experiment effect is good. The results of our study can be used in the classification, retrieval, and other fields of Flash animation based on emotional semantics.

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Section: Related Researchesmentioning

confidence: 99%

Emotional Semantic Recognition of Visual Scene in Flash Animation

Shi

Xiang-zeng

2018

Journal of Control Science and Engineering

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“…Video affective content is defined as the intensity and type of affect expected to arise in viewer while watching the video [1] . In order to identify affective content in a video, the relations between low-level video features and the basic emotion types and intensity are mostly built by the methods including the pattern classifier (HMMs or SVM) and rule-based reasoning, etc [2,3,4] .…”

Section: Introductionmentioning

confidence: 99%

Content-Based Affective Intensity Recognition Algorithm Using Unascertained Measure

Yan

Wen

Zheng

et al. 2010

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Affective content recognition is an unsolved technical problem in intricate video retrieval and high-level applications. In this paper , a few video retrieval problems are studied at the view of affective intensity recognition. A novel algorithm about video scene affective intensity recognition, which is based on the unascertained measure, is proposed. The algorithm consists of two parts. Firstly, the sub-module of video affective intensity features extraction introduces the affective intensity feature extraction sub-models of the shots correlation coefficient（SCC） and the duration ratio of the shots（DROS）. Secondly, the sub-module of affective intensity determination includes the unascertained intensity measure matrix of video scene and video affective intensity type recognition sub-model. The experimental results show that the proposed algorithm is feasible and achieves more than 70 percent accuracy.

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