A Sieving ANN for Emotion-Based Movie Clip Classification
| Autor: | Saowaluk C. Watanapa, Bundit Thipakorn, Nipon Charoenkitkarn |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Artificial neural network
business.industry Computer science media_common.quotation_subject Speech recognition Feature extraction Search engine indexing computer.software_genre Sadness Artificial Intelligence Hardware and Architecture Content analysis Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business computer Software Natural language processing media_common |
| Zdroj: | IEICE Transactions on Information and Systems. :1562-1572 |
| ISSN: | 1745-1361 0916-8532 |
| DOI: | 10.1093/ietisy/e91-d.5.1562 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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