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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳宏銘(Homer H. Chen) | |
| dc.contributor.author | Keng-Sheng Lin | en |
| dc.contributor.author | 林耿生 | zh_TW |
| dc.date.accessioned | 2021-06-17T00:11:33Z | - |
| dc.date.available | 2015-07-18 | |
| dc.date.copyright | 2012-07-18 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-07-12 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65776 | - |
| dc.description.abstract | 因為戲劇節目中富含情感的段落往往是最吸引觀眾的部分,所以利用情緒為 基礎的精采片段擷取系統對於戲劇影片檢索和預告片生成是相當有助益的。在本 篇論文當中,我們將精采片段擷取公式化成迴歸的問題,並利用迴歸理論預測影 片片段引發觀眾情感的程度。有別於一般系統從實驗性的觀察中定義試誤性的規 則,本系統利用機器學習決定精采片段與影音特徵的關係。此外,我們從心理學 和戲劇學的角度分析戲劇節目的特性以提出與精采片段相關的影音特徵:人臉、 音樂情緒、鏡頭長度、動作幅度。最後,我們利用量化的方式分析本系統在精采 片段擷取上的準確度。 | zh_TW |
| dc.description.abstract | Emotion-based highlights extraction is useful for retrieval and automatic trailer generation of drama video because the rich emotion part of a drama video is often the center of attraction to the viewer. In this thesis, we formulate highlights extraction as a regression problem to extract highlight segments and to predict how strong the viewer’s emotion would be evoked by the video segments. Unlike conventional rule-based approaches that rely on heuristics, the proposed system determines the relation between drama highlights and audiovisual features by machine learning. We also examine the special characteristics of drama video and propose human face, music emotion, shot duration, and motion magnitude as feature sets for highlights extraction. Quantitative evaluation results are provided to illustrate the performance of the system. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T00:11:33Z (GMT). No. of bitstreams: 1 ntu-101-R99942041-1.pdf: 1161070 bytes, checksum: d3aa41ab4a5ad21d703f61e1ed408620 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 口試委員會審定書...#
誌謝 ...i 中文摘要 ...ii ABSTRACT ...iii CONTENTS ...iv LIST OF FIGURES ...vi LIST OF TABLES ...vii Chapter 1 Introduction...1 Chapter 2 Related Work...4 2.1 Rule-Based Approach ...4 2.2 Attention-Based Approach...5 Chapter 3 Regression-Based Approach...6 Chapter 4 System Description...8 4.1 Data Collection ...9 4.2 Subjective Test ...9 4.3 Regressor Training...10 4.4 Forming the Highlight Sequence...11 Chapter 5 Music Detection and Music Emotion Recognition...12 5.1 Adaptive Music Detection and Identification by Audio Fingerprint ...13 5.2 Music Emotion Recognition...16 Chapter 6 Visual Features Extraction...17 6.1 Human Face ...17 6.2 Shot Duration...19 6.3 Motion Magnitude ...20 Chapter 7 Experimental Results...22 7.1 Evaluation of Feature Fusion...23 7.2 Evaluation of Feature Combination...24 7.3 Evaluation of the Whole System ...26 Chapter 8 Discussion...27 Chapter 9 Conclusion ...28 REFERENCE ...29 Appendix ...34 Drama Dataset...34 | |
| dc.language.iso | en | |
| dc.subject | 戲劇節目 | zh_TW |
| dc.subject | 精采片段 | zh_TW |
| dc.subject | 情感內容 | zh_TW |
| dc.subject | 迴歸 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | drama video | en |
| dc.subject | highlights extraction | en |
| dc.subject | regression | en |
| dc.subject | machine learning | en |
| dc.subject | Affective content | en |
| dc.title | 利用機器學習之影片精彩片段擷取系統 | zh_TW |
| dc.title | Learning-Based Video Highlights Extraction | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡振家,杭學鳴,張寶基,林嘉文 | |
| dc.subject.keyword | 情感內容,精采片段,戲劇節目,機器學習,迴歸, | zh_TW |
| dc.subject.keyword | Affective content,highlights extraction,drama video,machine learning,regression, | en |
| dc.relation.page | 35 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-07-12 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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