臺灣博碩士論文加值系統

近年來在雲端計算的熱潮之下，多媒體內容的提供變得非常普及，諸如YouTube，CNN，BBC等等。在資訊爆炸的時代下，作為一個好的線上服務提供者，必然需要為使用者精準地檢索，並重新組織內容後再呈現。在一個基於內容比對的影片檢索系統中，有兩個迫切的需求：理解多媒體內容，和重新組織多媒體內容。一個很好的範本是CNN的專題報導(special report)。新聞影片在經過人工比較後，將相關影片列在同一個網頁上。本論文著眼於開發一個以專題報導為目標，但不需人工介入的系統。為了理解影片內容，我們利用強健的視覺特徵來探知畫面中出現的概念。為了重新組織檢索結果，系統需要抽取出事件相關的詞彙(relevant term)。系統亦需要為檢索得到的影片產生有意義的簡短預覽影片(brief preview)，以利線上快速預覽。
近年來，研究學者們利用視覺概念探知來填補理解視覺內容的語意缺口。視覺概念可藉由視覺文字(Visual Word, VW)探知。視覺文字在過去研究中，被證實有不錯的正確率。然而當一個畫面中包含多個概念時，會劇烈降低探知的準確率。本論文提出基於概念的視覺文字分群法(Concept-based Visual Word Clustering, CVWC)，在未經事前切割的圖片上探知出現的概念。我們利用網路圖片周圍的文字訓練出概念之間的預知識(prior knowledge)。為了求得分群法的近似最佳解，我們提出概念式基因演算法(concept-based genetic algorithm,CBGA)，根據視覺文字在所屬概念中之重要性，以及兩兩概念共同出現的機率來求解。CBGA亦利用空間上周圍的視覺文字來探知概念。概念延伸(concept extension, CE)接著從目前的分群結果中，更新可能探知的概念集合。
視覺片語(visual phrase)是一個視覺文字的集合，它提升了視覺概念探知的準確率，但在真實影片中應用時仍稍嫌不足。由於一個物件的圖片可能擁有多重視角，在過去方法取出之視覺片語中，部份可能會在某些角度下被遮蔽，而影響視覺片語的抽取結果。本論文利用圖形探勘(graph mining)來取出強健的視覺片語。視覺文字在每張屬於同一個分類圖片的出現頻率(appearance frequencies)，分別用來建立起一個個的視覺文字關係圖。圖形探勘便從這些關係圖中，探勘出部份的子圖。這些子圖的出現密集(dense)和出現頻率(frequency)皆超出指定的門坎值(threshold)，並視為視覺片語。
事件相關的詞彙(relevant term)時常在傳統TFIDF方法下得到較低的逆向文件頻率(inverse document frequency)，而被誤判為常見詞彙。本論文提出基於視窗文件頻率分佈的詞彙計算權重方式，來強調事件相關詞彙的分數。套用滑動視窗(sliding window)於頻率分佈上，計算出視窗文件頻率分佈。藉由箱型圖(boxplot)，可以將分佈上的視窗文件頻率分為尋常和不尋常兩個詞彙群組。依此兩個群組的差值，可計算出指定詞彙的相關詞彙權重。
新聞影片中，主播片段的文字是經過人工摘要過的精簡內容。藉由匹配(alignment)主播片段的文字和外場富含事件意義的畫面，可以產生適當的簡短預覽影片。匹配這兩種媒體並不如想像中容易，主因有二：畫面上精準找到物件並不容易，而且文字詞彙常有同義詞而引發錯誤匹配。本論文利用視覺上的語言模型來描述畫面在時間上的關係，並套用在以句子為單位的匹配方法。對於資料庫每則新聞影片皆取出關鍵畫面，和畫面上的主要物件。單字集(Bag-of-Words)表示法接著將主要物件對應到視覺樣本(visual pattern)。文字詞彙同時利用知網(HowNet)對應到文字概念(textual concept)。最後利用IBM模型(IBM Model-1)考量視覺樣本間的語言模型，來匹配文字概念和視覺樣本。
本論文中每個提出的方法，皆實作於事件影片檢索系統中，以評估可行性。實驗結果顯示這些方法可以在事件影片檢索系統中得到改善。

In recent years, multimedia content has been used on video-sharing Web sites, such as YouTube, CNN, and the BBC. As a useful online aid for information, the content should be retrieved accurately and reorganized heuristically for users. In a content-based video retrieval system, two issues are in emergent need: content understanding and content organization. Special reports in news videos are a template for online services. Related videos are shown on the same Web page, and the content is compared with similar concepts manually. For this dissertation, we develop a system of special reporting in news videos without human interference. To understand content, the concepts appearing in the video have strong visual features. For content reorganization, event-related terms must be extracted. A meaningful preview video for each video must be generated.
Visual concept discovery (CD) was used to fill the semantic gap for retrieving visual content. Visual words (VWs), which have a good accuracy rate, were applied to visual CD. However, multiple concepts in an image generally degrade the discovery accuracy. A concept-based visual word clustering (CVWC) method is proposed to discover multiple concepts in an image without presegmented training images. CVWC is based on prior knowledge of concepts, which are derived from the meta-text of Web images. Concepts are obtained by clustering VWs extracted from image segments. A concept-based genetic algorithm (CBGA) is used to search for near-optimal clusters by VWs and the co-occurrence probability of two concepts. The clustering procedure is performed on related VWs to discover all aspects representing a concept. A concept extension method (CE) is applied to iteratively update the discovered concepts from the cluster results.
Although visual phrases (VP), a set of VWs, improve the accuracy of visual CD, the VWs are not versatile enough. For images of objects with multiple angles, the VWs in a VP may overlap with other objects, thus degrading VP extraction performance. This dissertation presents an approach for effective visual phrase extraction using graph mining. A concurrent appearance of two VWs is estimated encompassing all category-related images in a database. The appearance frequencies of VWs in each image are then used to construct a relation graph of VWs. Graph mining is used to mine dense subgraphs from the VW relationship graphs to extract VPs.
Event-related terms, known as relevant terms (RTs), are commonly used terms and obtain lower inverse document frequencies when using a conventional term frequency-inverse document frequency (TFIDF) method. A term scoring method is proposed to enhance the weights of RT in an event by considering the windowed document-frequency distribution. The weight for a given term is determined by the mean of the difference between usual and unusual term categories, which are quantized using a boxplot method.
A preview of a news video is generated by semantically aligning the textual sentences of the anchor report, summarized by the anchor, with visual field report shots. Because accurately detecting an object in a visual shot is difficult, and a textual term may generally correspond to several synonyms, the alignment of an anchor sentence with a video shot remains challenging. The temporal relations among the frames in a visual shot are characterized by a visual language model. Language model-based temporal relationships are then applied to sentence-based alignments. The Bag-of-Word (BoW) representations for the main objects in the key frames of a visual shot are first mapped to the visual patterns obtained from the news-video database. Second, textual terms in the report sentence are mapped to the textual concepts that are obtained from the HowNet knowledge database. Finally, unsupervised alignment between the textual concepts and the visual patterns in the news videos is performed using the IBM model-1.
These methods were implemented in each component of an event-related video retrieval system for performance evaluation. The experimental results show that the proposed approaches achieved improvements in event-related video retrieval.

中文摘要 I
Abstract III
Acknowledgement V
Contents VII
List of Figures XI
List of Tables XIV
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Contributions 2
1.3 The Organization of this Dissertation 3
Chapter 2 Introduction to Concept Discovery for Video Retrieval 4
2.1 Concept Discovery 4
2.1.1 Concept Definition 5
2.2 Bag-of-Visual Words 6
2.2.1 Scale-Invariant Feature Transform (SIFT) (Lowe, 2004) 6
2.2.2 Visual Word (Sivic ＆ Zisserman, 2003) 8
2.2.3 Visual Phrase (Zheng et al., 2006) 9
2.2.4 Concept Detection 10
2.3 Video Retrieval 11
2.4 System Overview of This Dissertation 12
Chapter 3 Event-Related Term Extraction 14
3.1 Text-Based Video Retrieval 14
3.2 System Overview 16
3.2.1 Video Retrieval System 16
3.2.2 Video Segmentation and Keyframe Selection 17
3.2.3 Vector Space Model (Salton, 1975) 18
3.2.4 Property of Relevant Terms Distribution for an Event 18
3.3 Term Frequency – Relevant Document Frequency 19
3.3.1 Re-sampling 19
3.3.2 Boxplot-based Grouping 20
3.4 Summary of This Chapter 21
Chapter 4 Robust Visual Phrase Extraction 22
4.1 Visual Phrase Extraction 22
4.2 System Framework 23
4.3 Representation of Visual Word 24
4.4 Visual Phrase Extraction 25
4.5 Summary of This Chapter 27
Chapter 5 Multiple Visual Concept Discovery 28
5.1 Visual Concept Discovery 28
5.2 Overview of Multiple Visual Concept Discovery 31
5.3 Concept-Based Visual Word Clustering 34
5.3.1 Chromosome Representation 34
5.3.2 CBGA-Based Evolution 35
5.3.3 CBGA-Based Evaluation: Fitness Function 40
5.3.4 Concept Extension 40
5.4 Summary of This Chapter 41
Chapter 6 Brief Preview Video Generation 42
6.1 Multimedia Content Alighment 42
6.2 System Framework 47
6.2.1 Training 48
6.2.2 Test 49
6.3 Pre-Processing 49
6.3.1 Key term extraction 49
6.3.2 Key frame extraction 50
6.4 Textual Concept Discovery 51
6.4.1 HowNet term mapping 51
6.4.2 Unknown term mapping 52
6.4.3 Noun concept mapping 53
6.5 Visual Pattern Discovery 53
6.5.1 Visual word extraction 54
6.5.2 Object-of-Interest extraction 55
6.5.3 Visual pattern dictionary generation and visual pattern mapping 57
6.6 Concept-Based Alignment Model 57
6.7 Summary of This Chapter 61
Chapter 7 Evaluations 62
7.1 Database 62
7.2 Evaluations of Relevant Term Extraction 64
7.2.1 Experimental Setup 64
7.2.2 Analysis by Manually Generated Data 65
7.2.3 Analysis by Real Data 67
7.3 Evaluations of Visual Phrase Extraction 69
7.3.1 Experimental setup 69
7.3.2 Threshold edge , freq, and, dense 70
7.3.3 Evaluation of image retrieval 71
7.4 Evaluations of Multiple Concept Discovery 73
7.4.1 Experimental Setup 73
7.4.2 Estimation of CVWC Parameters 75
7.4.3 Case Study for Video Retrieval 81
7.5 Evaluations of Brief Preview Video Generation 87
7.5.1 Experimental setup 87
7.5.2 Evaluation of thresholds 88
7.5.3 Evaluation of alignment approaches 91
7.5.4 Evaluation of textual concept discovery 95
7.5.5 Evaluation of OOI extraction 96
Chapter 8 Conclusion and Future Work 98
8.1 Conclusion 98
8.2 Future work 100
Bibliography 101
Publication 108

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