臺灣博碩士論文加值系統

各種多媒體影像應用與技術的迅速發展，使得多媒體影像資料庫的規模日益增大，使用者很難透過簡單指令去達到有效地檢索這些大量影像資料庫。如何有效地從資料庫中找到特定的影像資料是一件很重要的議題，因此以影像內容為基礎的影像查詢技術被廣為研究。本論文提出兩種新的特徵擷取方法應用於影像查詢技術之研究。
本論文提出新的特徵擷取方法應用於影像查詢技術，不同於區域二元化型態和區域三元化型態，其主要是根據每一個像素點中的角度關係做為主要特徵。首先，分別累計每一個像數點中0∘、 45∘和90∘的角度變化量的特徵，並利用這些特徵透過粒子群演算法計算出最佳的量化門檻值及建造相對應的直方圖。之後，計算查詢影像與資料庫影像之量化後的直方圖做其差異度的比較。本篇方法的實驗結果證明，本研究所提出的查詢影像技術比傳統的區域二元化型態和區域三元化型態的方法有更高的平均查詢準確率。
本論文提出的第二個方法是以多樣性的圖樣描述來擷取方向性紋理特徵。利用10種多樣性的圖樣來擷取影像中紋理及顏色特徵，而這10種圖樣中的每一種圖樣分別代表2×2 區塊中的每一種邊緣型態。首先，將彩色影像轉換至HSV顏色圖層。接著，針對量化後色相及飽和度擷取主要的顏色特徵，而量化後亮度層則由多樣性圖樣擷取紋理特徵，將兩種特徵結合後比較查詢圖與資料庫影像的差異值。經由實驗結果顯示，本文方法在查詢率及準確率方面的具有較佳效果。

With the advances in various multimedia technologies, an explosive growth of multimedia databases and digital libraries are being produced constantly. These large collections of images make difficult for users to search and browse efficiently through the entire database. Therefore, how to get the specific images from the databases is a crucial problem. In this thesis, there are two retrieval methods proposed to deal with this problem.
A directional local pattern-based variability histogram and particle swarm optimization (PSO) is proposed. This method extends the standard local binary pattern (LBP) and local ternary pattern (LTP) by using the directional relationship between the central pixel and its neighbors. Directional local pattern is a structure to encode directional features based on local variations. Firstly, the image is computed to obtain the different values of each pixel with its neighbors in each direction of 0°, 45° and 90° respectively. Then, collect gray-level differences and use PSO algorithm to compute the quantized thresholds. After the above process, construct the variability histogram for the directional local patterns to present the texture feature. Finally, we calculate the similarity distance between query image and database images. And the expected experimental results indicate that our algorithm performs much better than traditional LBP and LTP do in terms of average retrieval rate (ARR).
The multi-element descriptor of texture construction features for content-based image retrieval (CBIR) is the second proposed method. This method extracts color feature and texture features by multi-element descriptor which is constructed by ten element types. Each type denotes individual directional state of 2×2 grid. Firstly, the color image is converted into HSV color space. Then, extract primary color feature from quantized Hue and Saturation components and use multi-element descriptor to extract texture feature from quantized Value component. If extracted texture feature is conformed to the regional threshold, then the color feature of corresponding position can be saved. Finally, combine color feature with texture feature and compute the similarity distance between query image and database images. By the experiments, the proposed method is more efficient than the structure elements descriptor and micro-structure descriptor. The proposed method is tested to Corel-1,000 database and Corel-10,000 database. The experimental results indicate that our algorithm has much better and effective performance in terms of average precision and average recall.

Abstract in Chinese i
Abstract in English iii
Acknowledgement v
List of Figures vii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Image Features 3
1.3 Thesis Organization 5
Chapter 2 Related Works 6
2.1 Local Binary Pattern 6
2.2 Local Ternary Pattern 8
2.3 Particle Swarm Optimization Algorithm 10
Chapter 3 Content-based Image Retrieval Using Directional Local Patterns and Particle Swarm Optimization Algorithm 12
3.1 The Proposed Scheme 12
3.1.1 Directional feature extraction 13
3.1.2 Evaluation the optimized quantized thresholds of quantifying 17
3.1.3 Quantization histogram 20
3.2 Proposed System Framework 22
3.3 Similarity Measurement 23
3.4 Experimental Results 24
3.5 Summary 27
Chapter 4 Image Retrieval Using Multi-element Descriptor of Texture Construction Features 28
4.1 The Proposed Scheme 28
4.1.1 RGB to HSV conversion and quantization 30
4.1.2 Features extraction and multi-elements descript 33
4.1.3 Features construction 38
4.2 Proposed System Framework 40
4.3 Similarity Distance Measure 41
4.4 Experimental Results 42
4.5 Summary 53
Chapter 5 Conclusions and Future Works 55
5.1 Conclusions 55
5.2 Future Works 56
Bibliography 57

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