臺灣博碩士論文加值系統

隨著數位資訊的迅速發展，如何有效的發展一套多媒體檢索系統已經成為一個很重要的議題。近年來，以數位資訊內容為基礎之多媒體檢所系統（content-based multimedia retrieval system）一直是一個很熱門的研究。許多的檢索系統提出了很多的特徵將多媒體資訊呈現出來，如何有效的將目前現有的特徵整合起來，已經變成另一個更重要的議題。
在本論文中提出了一個新的特徵整合演算法，讓系統自動去挑選相關的資訊（automatic relevance selecting method, ARSM），根據系統所挑選出來的相關資訊結合查詢點移動（query point movement, QPM）、分數演算方法（grade evaluation method, GEM）和重新權重特徵整合方法（re-weighting feature combination, RFC）等，使用上述特徵整合方法計算特徵權重及重新修改查詢物件的特徵向量，將目前系統的特徵有效的整合，進而改善檢索的結果。

The rapid generation on digital information has made the development of efficient multimedia retrieval tools become urgently. In recent year, content-based multimedia retrieval system becomes the popular research. Various features are proposed to extract the diverse characteristic of multimedia data. These features can be integrated to improve accuracy of content-based multimedia retrieval system. We propose the automatic relevance selecting method (ARSM) to automatically determine the relevant information for improving retrieval result. The ARSM is used to individually combine with query point movement (QPM), grade evaluation (GEM) and re-weighting feature combination (RFC) methods. Finally, our feature integration method is used to automatically determine feature weighting and modify query feature vector. Experimental results show that proposed method is superior to others.

Abstract I
CONTENTS II
List of Figures III
List of Tables IV
Chapter 1 1
Introduction 1
Chapter 2 5
Related works 5
2.1 Rocchio’s formula 5
2.2 MARS 6
2.3 Query Expansion method 7
2.4 Qcluster method 7
2.5 Basestar system 8
2.6 Soft relevance feedback 9
2.7 FRM method 11
2.8 Relevance score method 11
2.9 Mann–Whitney test method 12
2.10 Comparison FRM and RSM 13
2.11 Two-level relevance feedback mechanism 14
2.12 Data fusion 14
2.12.1 Rank Position 14
2.12.2 Borda count method 15
2.12.3 Condorcet method 16
Chapter 3 18
The proposed method for feature integration 18
3.1 Automatically select relevance 3D model 18
3.2 Grade Evaluation Method (GEM) 20
3.3 re-weighting feature combination (RFC) 22
3.4 Query Point Movement method (QPM) 23
Chapter 4 26
Experimental result 26
Chapter 5 33
Conclusion 33
Reference 34

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