A sieving ANN for emotion-based movie clip classification
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Publication Details
Author list: Watanapa S.C., Thipakorn B., Charoenkitkarn N.
Publisher: Institute of Electronics, Information and Communication Engineers
Publication year: 2008
Journal: IEICE Transactions on Information and Systems (0916-8532)
Volume number: E91-D
Issue number: 5
Start page: 1562
End page: 1572
Number of pages: 11
ISSN: 0916-8532
eISSN: 1745-1361
Languages: English-Great Britain (EN-GB)
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Abstract
Effective classification and analysis of semantic contents are very important for the content-based indexing and retrieval of video database. Our research attempts to classify movie clips into three groups of commonly elicited emotions, namely excitement, joy and sadness, based on a set of abstract-level semantic features extracted from the film sequence. In particular, these features consist of six visual and audio measures grounded on the artistic film theories. A unique sieving-structured neural network is proposed to be the classifying model due to its robustness. The performance of the proposed model is tested with 101 movie clips excerpted from 24 award-winning and well-known Hollywood feature films. The experimental result of 97.8% correct classification rate, measured against the collected human-judges, indicates the great potential of using abstract-level semantic features as an engineered tool for the application of video-content retrieval/indexing. Copyright ฉ 2008 The Institute of Electronics, Information and Communication Engineers.
Keywords
Emotion-based classification, Movie clip classification, Multimedia content analysis, Semantic content analysis, Video analysis
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