臺灣博碩士論文加值系統

近幾年來，隨著影像資料量的增加，我們需要有效的方法去建檔，搜尋，和擷取這些影像。所以在電腦視覺和多媒體方面，以內容為基礎的影像擷取已經佔有很重要的領域。影像擷取分成兩部分，一是以整張影像內容去做搜尋，二是以區域為基礎去做搜尋。用這些區域可以提供搜尋一張影像裡面某些特定的內容。
本論文提出一個以區域為基礎的互動式影像擷取系統。首先，我們利用顏色分群以及標記法將影像切割成許多區域。接著我們擷取出一些具有不受幾何轉換影響的特徵來描述每個區域。這些特徵包括：主要顏色，顏色統計圖，形狀特徵 (moment invariants) 以及材質特徵。之後，我們使用這些區域的特徵向量來代表每張影像。
由於每個特徵去有不同的重要性，且影像內每個區域所佔的比重也不相同;因此我們定義一階層式的影像距離評量方法。透過互動式的回饋查詢，我們可以計算出理想中的區域特徵以及區域內每個特徵之間的比重。
我們提供三種不同的搜尋方式: 以一張影像搜尋，以某一個特定的區域搜尋，將區域組合去搜尋。我們做了很多實驗證明上述方法的有效性，並比較經由三種不同的搜尋方式得出的實驗結果，實驗結果顯示透過此方法，的確可以找到比較理想的搜尋結果。

This thesis proposes an interactive region-based image retrieval system. Initially, we use color clustering by K-means algorithm and region labeling to segment an image into regions. Several geometric invariant features, such as dominant color, color histogram, moment invariants, and co-occurrence texture features, are extracted from regions. Then, we describe each image as a combination of feature vectors of the segmented regions.
To measure the image distance, we define a hierarchical distance function as a liner combination of region features. The retrieved results can be refined via interactive relevance feedback. To learn the “ideal” query regions that the users really want, we derive the weighting parameters of distance measurement using optimized learning technique.
A series of experiments on three query types demonstrate that the effectiveness of our work.

1. Introduction 1
2. Previous Work and Backgrounds 3
2.1 Feature extraction and matching 3
2.1.1 Color 3
2.1.2 Texture 4
2.1.3 Shape 4
2.2 Relevance Feedback 5
2.3 Image Retrieval Systems 6
3. Region Segmentation by Color Clustering and Region Labeling 8
3.1 Color clustering in different color spaces 8
3.2 K-Means with connectivity constraint algorithm (KMC) 12
3.3 Color Clustering by K-means algorithm 15
3.4 Region Labeling 19
4. Feature Extraction and Indexing 21
4.1 Dominant Color 21
4.2 Color histogram 22
4.3 Shape 22
4.4 Texture 24
5. Retrieval with Relevance Feedback 27
5.1 Feature distance measurement 27
5.2 Relevance feedback by a single region 29
5.3 Relevance feedback by multiple regions 31
6. Experimental results and discussion 33
6.1 Query by an image 35
6.1.1 Diagonal Euclidean matrix 35
6.1.2 General Euclidean matrix 39
6.2 Query by a single region 41
6.1.1 Diagonal Euclidean matrix 41
6.2.2 General Euclidean matrix 45
6.3 Query by multiple regions 48
6.3.1 Diagonal Euclidean matrix 48
6.3.2 General Euclidean matrix 51
7. Conclusions and Future Work 54
8. Reference 55
APPENDIX 58

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