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姓名	周凡迪(Fan-Di Jou)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	直方統計圖之深入分析探討及其於影像檢索之應用 (Comprehensive Analysis of Histograand Its Application to Image Retrieval)
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摘要(中)	直方統計圖的比對是圖形識別的相關應用中常需使用到的技術，比對兩個不同的圖樣可藉由比對相對應的直方統計圖來完成。在本篇論文中，我們將深入分析直方統計圖的特質，並且將之應用在影像檢索技術的提升。我們所探討的主題依次是 一、直方統計圖的空間包容度的分析。在此我們提出了一個公平的比較方法，可以利用直方統計圖的空間包容度的量測來比較各種不同特徵的直方統計圖或不同量測函數的鑑別能力。 二、直方統計圖的模糊化技術。藉由模糊化技術可以克服由於直方統計圖統計圖的鑑別力提升卻造成相似圖樣的距離同時被拉大的副作用。 三、自動化的相似度判斷機制。可以取代傳統上以人工方式評估影像檢索系統效能的缺點，是一個客觀而且能夠量化統計分析影像檢索檢索效能的技術。 四、大型直方統計圖的比對演算法。我們提出了一個適用於大部分常見量測函數與直方統計圖的大小無關的比對演算法。藉由我們的技術，直方統計圖的鑑別能力可藉由提高特徵數或各個特徵的解析度自由提升，而不必擔心直方統計圖大小對效率的影響。 最後本論文以數個影像檢索的實驗分析驗證我們提出的方法，並做出總結。
摘要(英)	Histogram matching is a commonly adopted technique in the applications of pattern recognition. The matching of two patterns can thus be accomplished by matching their corresponding histograms. In this dissertation, we comprehensively analyze the characteristics of histogram and discuss the issues frequently appeared in image retrieval by utilizing the detail analyzing results. The characteristics inherent in histograms that we study are histogram capacity and histogram smoothing. Through the analysis of histogram capacities, researchers can evaluate and compare the discrimination capabilities of different histogram measurements or histograms embedded with different features. As to the histogram smoothing technique, it can compensate the penalty resulting from the enlargement of histogram dissimilarities of similar patterns due to the increase of discrimination capability. Moreover, an automatic similarity judgment mechanism without human involving for the evaluation of the retrieval effectiveness of image retrieval algorithms is proposed. Traditionally, researchers usually use the statistic scores of similarity judgments deriving from a few persons to demonstrate the performance of their image retrieval systems. However, the statistic scores of similarity judgment are too subjective. Through the use of our proposed similarity judgment mechanism, the retrieval effectiveness of image retrieval systems can be evaluated objectively, transparently and quantitatively. As we know, the number of features and the resolution of each feature will determine the size of histograms. Usually, the more the number of features and the higher the resolution of each feature, the stronger the discrimination capability of histograms will be. Unfortunately, it will lead to the decrease of the efficiency of histogram matching because traditional algorithms in evaluating similarity are all relevant to the histogram size. In this dissertation, a novel histogram-matching algorithm is proposed whose efficiency is irrelevant to the histogram size. By adopting our proposed algorithm, future researchers can focus more on the selection and combination of histogram features and freely adjust the resolution of each feature without worrying the decrease of retrieval efficiency. To demonstrate the feasibility and validity of the analyzing results and the proposed algorithms, a similar image retrieval experiment, which was constructed based on multi-feature histogram, is conducted. Although the experiment itself is the image retrieval application, the proposed histogram-relating techniques are not merely limited to be employed to image patterns. They can be applied to other retrieval-like applications which are independent of images or even other fields irrelevant to retrieval-like applications.
關鍵字(中)	★ 影像檢索 ★ 直方統計圖	關鍵字(英)	★  large-size histogram matching ★ similarity judgment ★ histogram smoothing ★ histogram capacity ★ image retrieval
論文目次	ABSTRACT i CONTENTS iii LIST OF FIGURES vi LIST OF TABLES viii LIST OF EQUATIONS ix LIST OF DEFINITIONS xiv LIST OF ALGORITHMS xv CHAPTER 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Review of Relative Works 8 1.3 Organization of the Dissertation 12 CHAPTER 2 FOUNDATIONS OF HISTOGRAM 13 2.1 Definition and Notation 13 2.2 Histogram Similarity Measurements 18 CHAPTER 3 COMPREHENSIVE ANALYSIS OF HISTOGRAM CHARACTERISTICS 23 3.1 Histogram Capacity 24 3.1.1 The Capacity of Histogram Space 24 3.1.2 Fair Comparison between Dissimilarity Measurements 28 3.2 Histogram Smoothing 37 3.2.1 Histogram Smoothing 37 3.2.2 Quantized Average Smoothing Histogram 42 3.3 Concluding Remarks and Discussion 53 CHAPTER 4 QUANTITATIVE ANALYSIS FOR SIMILAR IMAGE RETRIEVAL 55 4.1 Motivation 55 4.2 Video Indexing 57 4.2.1 Introduction of Video Indexing Problem 57 4.2.2 Video Segmentation 60 4.2.3 Key Frame Extraction 63 4.3 Automatic Mechanism for Similarity Judgment 68 4.4 Concluding Remarks and Discussion 84 CHAPTER 5 EFFICIENT MATCHING FOR LARGE MULTI-FEATURE HISTOGRAM IN IMAGE RETRIEVAL 87 5.1 Multi-Feature Histogram 88 5.2 Efficient Histogram-matching Algorithm 91 5.2.1 Traditional Algorithm 91 5.2.2 Vinod’s Algorithm 94 5.2.3 Our Approaches 98 5.3 Concluding Remarks and Discussion 104 CHAPTER 6 EXPERIMENTAL RESULTS 107 6.1 Scenario 107 6.1.1 Thumbnail Images Scheme 108 6.1.1 Measurement Functions in Experiment 109 6.1.2 Experimental Histograms 110 6.2 Experimental Results 111 6.2.1 Comparison of Retrieval Effectiveness between Histograms with Different Histogram Sizes 112 6.2.2 Comparison of Retrieval Efficiency between Efficient Matching and Traditional Method 114 6.2.3 Comparison of Retrieval Effectiveness plus the Quantized Average Smoothing Histogram Method 116 6.2.4 Comparison of Retrieval Effectiveness between Images with Different Sizes 119 CHAPTER 7 CONCLUSIONS 121 7.1 Concluding Remarks 121 7.2 Future Works 125 REFERENCES 127 APPENDIX 141 Appendix A 141 Appendix B 143 Appendix C 144 Appendix D 146
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指導教授	范國清(Kuo-Chin Fan)	審核日期	2003-7-18
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