臺灣博碩士論文加值系統

近年來，數位科技的進步造成多媒體資料大量成長，如：影像、視訊、音樂等多媒體資料，加上網路通訊的進步使得多媒體數量更加龐大，分佈更加廣泛。因此，如何將多媒體資料概念化，並作有效率的存取及推薦，已成為近年來一個極具吸引且具挑戰性的議題。為了處理此議題，本研究的主要目的在於發展多媒體知識挖掘技術，從多媒體資料中找出有價值之知識，並藉此達到高品質的多媒體註解、搜尋與推薦。
在多媒體註解的領域中，由於視覺特徵與語意概念的不確定性，造成了視覺特徵與語意概念之間的鴻溝難以縮減。所謂的「視覺特徵與語意概念的不確定性」指的是在人類的不同的語意概念上常存在著相同的視覺特徵。為了減輕此問題所帶來的影響，本研究首先挖掘存在視覺特徵、文字資訊與語音資訊中重要的訊息來完成影像或視訊的註解。關於影像註解方面，我們提出一以視覺特徵為基礎的註解分法，主要是要解決視覺特徵與語意概念的不確定性。此外我們亦針對網頁上的影像提出一以文字資訊為基礎的註解方法，其目的在找出影像註解字與網頁資訊之間的關聯性，以補足視覺特徵為基礎的註解缺點。關於視訊註解方面，我們找出視覺特徵之間、語音特徵之間、視覺與語音特徵之間的關係，並進一步考慮視訊所具有的時間連續性，藉以較正確地註解視訊影片。
基於上述影像註解的結果，人類的語意概念便能與視覺特徵關聯起來，以達成傳統以文字為基礎的影像搜尋。然而，較少影像搜尋系統直接從查詢影像到語意概念偵測，再從偵測的語意概念搜尋相關影像。因此，我們在此研究中提出了整合3種方法的影像搜尋系統來達成概念式的影像擷取，其中包括：影像註解，語意概念比對與模糊排序等方法。除了以語意概念為基礎的影像搜尋外，本研究亦提出內涵式的視訊擷取技術來補足以語意概念為基礎的搜尋所造成的缺失。在本研究提出的內涵式視訊擷取技術中，最重要的即是找出視訊與視訊間具有時間連續性與視覺相似性的樣式，如此可減低運算的時間與增加查詢的準確率。
相對於上述被動的影像或視訊搜尋技術，本研究亦發展一音樂推薦技術，主動提供使用者有興趣的音樂。本技術主要整合音樂內涵與協同式資訊過濾技術，並從使用紀錄的音樂內涵中萃取出使用者聽覺上的喜好樣式。藉由此喜好的聽覺樣式，音樂推薦不再受限於傳統評分多樣性的問題。綜觀以上所述之技術，本研究在多媒體註解、搜尋與推薦上提供了良好的解決方案，並可將這些技術擴充至其他多媒體應用，如行動式的多媒體擷取與推薦等。

In recent years, the advance of digital capturing technologies lead to the rapid growth of multimedia data in various formats, such as image, music, video and so on. Moreover, the modern telecommunication systems make multimedia data widespread and extremely large. Hence, how to conceptualize, retrieve and recommend the multimedia data from such massive multimedia data resources has been becoming an attractive and challenging issue over the past few years. To deal with this issue, the primary aim of this dissertation is to develop effective multimedia data mining techniques for discovering the valuable knowledge from multimedia data, so as to achieve the high quality of multimedia annotation, retrieval and recommendation.
Nowadays, a considerable number of studies in the field of multimedia annotations incur the difficulties of diverse relationships between human concepts and visual contents, namely diverse visual-concept associations. So-called visual-concept diversity indicates that, a set of different concepts share with very similar visual features. To alleviate the problems of diverse visual-concept associations, this dissertation presents the integrated mining of visual, speech and text features for semantic image/video annotation. For image annotation, we propose a visual-based annotation method to disambiguate the image sense while a number of senses are shared by a number of images. Additionally, a textual-based annotation method, which attempts to discover the affinities of image captions and web-page keywords, is also proposed to attack the lack of visual-based annotations. For video annotation, with considering the temporal continuity, the frequent visual, textual and visual-textual patterns can be mined to support semantic video annotation by proposed video annotation models.
Based on the image annotation, the user’s interest and visual images can be bridged semantically for further textual-based image retrieval. However, little work has highlighted the conceptual retrieval from textual annotations to visual images in the last few years. To this end, the second intention in this dissertation is to retrieve the images by proposed image annotation, concept matching and fuzzy ranking techniques. In addition to textual-based image retrieval, the textual-based video retrieval cannot earn the user’s satisfaction either due to the problems of diverse query concepts. To supplement the weakness of textual-based video retrieval, we propose an innovative method to mine the temporal patterns from the video contents for supporting content-based video retrieval. On the basis of discovered temporal visual patterns, an efficient indexing technique and an effective sequence matching technique are integrated to reduce the computation cost and to raise the retrieval accuracy, respectively.
In contrast to passive image/video retrieval, music recommendation is the final concentration in this dissertation to actively provide the users with the preferred music pieces. In this work, we design a novel music recommender that integrates music content mining and collaborative filtering to help the users find what she/he prefers from a huge amount of music collections. By discovering preferable perceptual-patterns from music pieces, the user’s listening interest and music can be associated effectively. Also the traditional rating diversity problem can be alleviated. For each proposed approach above, the experimental results in this dissertation reveal that, our proposed multimedia data mining methods are beneficial for better multimedia annotation, retrieval and recommendation so as to apply to some real multimedia applications, such as mobile multimedia retrieval and recommendation.

ABSTRACT VI
誌 謝 IX
Chapter 1 1
Introduction 1
1.1 Motivation 3
1.2 Contributions 4
1.3 Dissertation Organization 7
Chapter 2 8
Background and Related Work 8
2.1 Video Annotation 8
2.2 Image Annotation 10
2.3 Image Retrieval 13
2.4 Video Retrieval 14
2.5 Music Recommendation 16
Chapter 3 18
Integrated Mining of Visual Features, Speech Features and Frequent Patterns for Semantic Video Annotation 18
3.1 Introduction 18
3.2 The Proposed Video Annotation Method 20
3.2.1 Preprocessing Operation 22
3.2.2 Training Phase 23
3.2.3 Prediction Phase 30
3.3 Empirical Evaluation 35
3.3.1 Experimental Data and Parameter Settings 35
3.3.2 Experimental Results 36
3.3.3 Discussions 42
Chapter 4 45
Intelligent Web Image Annotation for Semantic Image Retrieval 45
4.1 Introduction 45
4.2 Intelligent SeMantic Image explorER (iSMIER) 48
4.2.1 Overview of iSMIER 48
4.2.2 Training Phase 50
4.2.3 Query Phase 61
4.3 Experimental Evaluations 68
4.3.1 Evaluations of Annotation Models 68
4.3.2 Examinations of Concept Matching Model 73
4.3.3 Demonstrations of System Prototype 76
Chapter 5 77
Effective Content-based Video Retrieval Using Pattern Indexing and Matching Techniques 77
5.1 Introduction 77
5.2 The Proposed Content-Based Video Retrieval 79
5.2.1 Basic Idea 80
5.2.2 Overview of The Proposed Method 81
5.2.3 Preprocessing Stage 82
5.2.4 Indexing Stage 87
5.2.5 Search Stage (AFPI-Search) 92
5.3 Experimental Evaluations 96
5.3.1 Experimental Data 96
5.3.2 Experimental Results 98
Chapter 6 107
A Novel Music Recommender by Discovering Preferable Perceptual-Patterns from Music Pieces 107
6.1 Introduction 107
6.2 The Proposed Music Recommender 109
6.2.1 Offline Pre-processing 111
6.2.2 Online Prediction 117
6.3 Experimental Evaluations 122
6.3.1 Experimental Settings 122
6.3.2 Experimental Results 123
Chapter 7 127
Conclusions and Future Work 127
7.1 Conclusions 127
7.2 Future Work 129
References 131

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