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研究生: 吳文博
論文名稱: 基於自動建置的社群網路之電影中的人臉分群研究
Face Clustering in Movies Using Automatically Constructed Social Networks
指導教授: 葉梅珍
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2012
畢業學年度: 100
語文別: 中文
論文頁數: 45
中文關鍵詞: 人臉分群社群網路
英文關鍵詞: Face Clustering, Social Network
論文種類: 學術論文
相關次數: 點閱:118下載:10
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  • 傳統基於視覺的低階臉部特徵(Low-Level Face Descriptor)用於人臉分群(Face Clustering)的研究已有局限,主要的困難在於人臉在拍攝角度或亮度有差異的情況下,如何適當地衡量它們之間的相似程度,以避免在執行人臉分群的工作時,將同一個人的人臉影像分至不同人的人臉群集之中。為了解決角度亮度及人臉分群的問題,直覺的做法是需要人力的介入去檢視所有的人臉群集,並手動將相同的人臉群集合併在一起。在本篇論文中,我們提出藉由聯想預測模型(Associate-Predict Model)的方法與社群網路的特徵來解決這兩個問題。我們提出自動的方法,從電影中建立角色間的社群網路,再者,我們獲取社群網路內在所蘊含有意義的社群資訊,將這些資訊用於人臉分群的工作上以提升其準確率。我們實驗證明藉由角色間的社群關係確實可以增進人臉分群及社群網路建置的效能。

    This paper describes a technique for clustering faces in movies. Traditional methods are based on low-level visual features; such approaches have limited performance because of large intra-personal variations. We propose a new approach that revises the similarity of two face clusters by the use of an associate-predict model and social relationships, which are automatically discovered from movies. Experimental results validate the effectiveness of the proposed method. In addition, our approach can be used to construct social networks between characters that describe their social relationships.

    中文摘要 ii 英文摘要 iii 誌謝 iv 目錄 v 附表目錄 vi 附圖目錄 vii 第一章 簡介 8 1.1 研究背景與動機 8 1.2 系統架構 9 1.3 文章架構 10 第二章 文獻探討 11 第三章 自動建置電影角色的社群網路 15 3.1 人臉軌跡 15 3.2 聯想預測模型 19 3.3 電影角色的社群網路 22 第四章 利用社群網路改善人臉分群 24 4.1 聯想預測模型於人臉分群的限制 24 4.2 社群網路特徵 27 4.2.1 高鑑別度的群集 29 4.2.2 有互動的社群關係 31 4.2.3 無互動的社群關係 33 4.3 特徵合併 34 第五章 實驗結果與分析 35 5.1 實驗設置 35 5.2 方法比較 36 5.2.1 基於臉部特徵與聯想預測模型及社群網路的分群結果比較 36 5.2.2 基於不同定義挑選重要群集的分群結果比較 37 5.2.3 基於互動關係的分群結果比較 38 5.3 社群網路的演化 39 5.4 實驗結果 40 第六章 結論 42 參考文獻 43

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