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研究生:陳暐岳
研究生(外文):Wei-Yueh Chen
論文名稱:以彩色聚類及空間資訊為主的影像檢索系統設計
論文名稱(外文):Image Query System Design Based on Color Clustering and Spatial Information
指導教授:范欽雄范欽雄引用關係
指導教授(外文):Chin-Shyurng Fahn
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:83
中文關鍵詞:模糊c-means群聚法彩色區塊圖像資料庫影像搜尋系統
外文關鍵詞:fuzzy c-means clusteringcolor blockpictorial databaseimage query system
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近年來,隨者電腦科技的日新月異,造就了數位化時代的來臨,使得成千上萬的圖像、文字得以藉此永久保存。但也因為如此,如何讓使用者在大型圖像資料庫中快速且有效地檢索相似影像,進而能夠達成隨選視訊的目標,是現今研究圖像資料庫科學家們所努力不懈奮鬥的原因。現存的圖像資料庫大部分都是以文字註解當作相似圖片的搜尋依據,此方法由於人類的介入而能容易達成高檢索成功率,但這需要花費資料庫維護人員的大量時間去建立影像之間的關聯性,且需要使用者的配合,因此,單純使用影像內容去檢索大型圖像資料庫的技巧漸漸受到大眾所重視。在本篇論文中,我們提出一個以顏色聚類及空間資訊為主的影像檢索系統來搜尋彩色的自然影像。在系統中我們先將彩色影像從RGB彩色空間轉換到HSI彩色空間,隨後我們使用fuzzy c-means演算法來聚類色彩資訊並將它們當作全域性的彩色特徵存入資料庫中。此外,我們採用彩色區域成長的方法獲得影像中的彩色區塊,再從中擷取四種特徵資訊—位置、大小、顏色、形狀當作圖像資料庫的索引,然後使用相似度測量函數來決定兩張影像之間的相似度。實驗結果顯示我們的系統確實可有效地檢索出所要的影像。

In recent years, computer techniques have progressed with each passing day, and they promote the advent of a digital epoch. Therefore, thousands of pictures and texts can be reserved forever by digitizing. However, how can let users retrieve similar images effectively in a large pictorial database to attain the realm of video on demand is the reason why scientists still work hard for the query methodology of the pictorial database today. Most of the existing image query systems are with the aid of annotations to retrieve similar images. They usually make a high successful retrieval ratio due to human intervening. But these systems require builders to maintain the pictorial database by constructing the relations among stored images. It is time-consuming and also resorts to the coordination of manual operations. Hence, the techniques of simply applying contents to retrieve images in a large pictorial database have been drawn much attention from the people. In this thesis, we propose an image query system using color clustering and spatial information to retrieve colored natural scene images. In the system, we transform the color information of an image from the RGB color space to the HSI color space, and then employ the fuzzy c-means algorithm to cluster the color information that are stored in the database and used as global color features. Besides, we adopt the color region growing method to obtain color blocks in the image. From the color blocks, we can extract four kinds of feature data: position, size, color, and shape that are served as the indices in our pictorial database. Then we can use a similarity measure function to evaluate the similarity between two images. The experimental results reveal that our system is feasible and effective to retrieve the images that we want.

CHAPTER 1 INTRODUCTION
1.1 Motivation
1.2 Survey of Previous Works and Literature Review
1.3 Thesis Organization
CHAPTER 2 REVIEWS OF RECENT IMAGE QUERY SYSTEMS
2.1 QBIC
2.1.1 Introduction
2.1.2 QBIC System Features and Capabilities
2.1.3 QBIC System Interface
2.2 VisualSEEk System
2.2.1 Introduction
2.2.2 VisualSEEk System Features and Capabilities
2.2.3 VisualSEEk System Interface
2.3 Photobook System
2.3.1 Introduction
2.3.2 Photobook System Features and Capabilities
2.3.3 Photobook System Interface
2.4 Bolbworld System
2.4.1 Introduction
2.4.2 Bolbworld System Features and Capabilities
2.4.3 Bolbworld System Interface
2.5 VIR System
2.5.1 Introduction
2.5.2 VIR System Features and Capabilities
2.6 MetaSEEk System
2.7 Image Query Systems Analysis
CHAPTER 3 IMAGE FEATURE EXTRACTION
3.1 Color Models and Conversion
3.1.1 The RGB color model
3.1.2 The HSI color model
3.1.3 Conversion between RGB and HSI
3.2 Color Image Segmentation
3.2.1 Fuzzy c-means
3.2.2 Color information cluster
3.2.3 Color blocks
3.2.4 Region growing
3.2.5 Invariant moments
3.3 Features Extraction
3.3.1 Globe-based color feature extraction
3.3.2 Region-based color blocks feature extraction
3.3.3 Feature Database
3.4 Features Extraction Methods Analysis
CHAPTER 4 IMPLEMENTATION OF IMAGE QUERY SYSTEM
4.1 Introduction
4.2 Pre-processing
4.3 Post-processing
4.3.1 Global Features Matching
4.3.2 Regionalized Features Matching
4.3.3 The Similarity Measure
4.4 User Interface
CHAPTER 5 EXPERIMENTAL RESULTS AND DISCUSSION
5.1 Introduction
5.2 Globe-based query
5.3 Region-based query
5.4 Synthetic query
5.4 Discussion
CHAPTER 6 CONCLUSIONS AND FUTURE WORK REFERENCE

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