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研究生:王雅羚
研究生(外文):Ya-ling Wang
論文名稱:以正交基底為基礎之Multiple-Instance影像資料擷取方法
論文名稱(外文):Multiple-Instance Learning Image Database Retrieval employing Orthogonal Fractal Bases
指導教授:蔣依吾蔣依吾引用關係
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊工程學系研究所
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:2004
畢業學年度:92
語文別:中文
論文頁數:77
中文關鍵詞:影像搜尋碎形正交基底
外文關鍵詞:Image RetrievalMultiple-Instance LearningFractal Orthonormal Basis
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  • 被引用被引用:3
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儲存於資料庫之資料,以往多僅限於文字,由於資訊多樣化,現今多轉變為多媒體資料,如何由數量龐大影像資料符合使用者條件,為使用者所殷切需求。目前以影像為鍵值進行影像資料庫索引之技術大致為根據影像中顏色、形狀、紋理、物體、結構等特徵建立影像資料庫索引檔,雖可適用特定處理領域CBIR(Content-Based Image Retrieval),但均無法保證所擷取特徵能符合(a) 高相關度影像資訊具高相關索引檔;(b) 索引檔相關度高,其影像資訊相關度亦高;(c) 索引檔相關度低,其影像資訊相關度亦低;(d) 影像資訊相關度低,其索引檔相關度亦低,此四項基本特質。
本論文使用碎形正交基底編碼(Fractal orthonormal bases)技術結合Multiple-Instance Learning,建立熱帶魚影像資料庫,每張資料庫內影像之特徵均由對碎形正交基底之投影向量值表示。正交基底是由碎形迭代函數透過target及domain blocks比對所訓練導出,可證明相似影像具相似碎形函數,而且不相似影像具相異碎形特徵向量;換言之,特徵點相距越遠,保證其對應影像內容一定不相似,然而特徵點較靠近,則保證其影像內容相似。因此,使用碎形正交基底函數線性組合所得係數為搜尋資料庫索引鍵值,可取得相似影像,並避免找出不相似影像。
由於欲搜尋之影像很難根據單一張搜尋影像(query image)代表所有可能之形狀、大小或方位,為使搜尋條件更為明確,藉由輸入多張與目標影像正、負相關搜尋影像,透過Multiple-Instance learning 法則自動地找出與正相關影像(positive examples) 相似且與負相關(negative examples)不相似之碎形正交基底投影向量特徵,使搜尋條件更為明確,將使用者最有興趣之部分,結合具有良好索引檔之碎形正交基底之技術。
影像比對時,方法是依據MIL所擷取之特徵,找尋資料庫哪些影像具有相似特徵,計算相似度,依此作排名輸出。詳細比對時,將資料庫中有著搜尋特徵之影像,找出該所屬區域,將擷取之特徵群正規化,求得每個特徵群佔所有搜尋特徵群之比例關係,再以依正相關特徵群之比例和資料庫影像特徵群比例,類似計算histogram之方式求得特徵比例相似度之外;另外還加入計算所求得特徵群之間結構關係,與正相關範例影像之特徵群結構關係亦計算特徵結構相似度;在加入每個特徵群區域之分散程度,及簡單計算其區域變異數亦和正相關範例做比較,於上述三者加入相似性量測中。
最後本論文將實作和此三種方法VQ, thumbnail fractal, multiscale entropy 為基礎,依其上述之影像特徵和碎形正交基底投影量之特徵,觀察這些影像特徵經MIL擷取共同之特徵群,再進行相同之比對依特徵群之分散程度、比例、結構關係量測,其搜尋結果之效能如何。
The objective of the present work is to propose a novel method to extract a stable feature set representative of image content. Each image is represented by a linear combination of fractal orthonormal basis vectors. The mapping coefficients of an image projected onto each orthonormal basis constitute the feature vector. The set of orthonormal basis vectors are generated by utilizing fractal iterative function through target and domain blocks mapping. The distance measure remains consistent, i.e., isometric embedded, between any image pairs before and after the projection onto orthonormal axes. Not only similar images generate points close to each other in the feature space, but also dissimilar ones produce feature points far apart. The above statements are logically equivalent to that distant feature points are guaranteed to map to images with dissimilar contents, while close feature points correspond to similar images.
In this paper, we adapt the Multiple Instance Learning paradigm using the Diverse Density algorithm as a way of modeling the ambiguity in images in order to learning concepts used to classify images. A user labels an image as positive if the image contains the concepts, as negative if the image far from the concepts. Each example image is a bag of blocks where only the bag is labeled. The User selects positive and negative image examples to train the concepts in feature space.
From a small collection of positive and negative examples, the system learns the concepts using them to retrieve images that contain the concepts from database. Each concept having similar blocks becomes the group in each image. According groups’ location distribution, variation and spatial relations computes positive examples and database images similarity.
第一章 影像搜尋之相關研究 1
1.1 良好影像搜尋系統 1
1.2現有影像搜尋系統 1
1.2.1 以顏色為特徵 2
1.2.2 以形狀為特徵 4
1.2.3 以內容物為特徵 6
1.3 研究概述 8
第二章 碎形基本理論 9
2.1 轉換之收歛性 11
2.2 迭代函數系統 (ITERATIVE FUNCTION SYSTEM) 11
2.3 影像分割 12
2.4 迭代函數 13
2.5 碎形在影像搜尋的應用 16
2.6 ORTHOGONAL BASIS IFS 17
定理證明 21
第三章 MULTIPLE-INSTANCE LEARNING 26
3.1 定義 26
3.2 MIL 應用於影像搜尋 26
3.2.1 應用實例 28
3.3 DIVERSE DENSITY ALGORITHM 33
3.3.1 Diverse Density definition: 34
3.3.2 計算 35
3.3.3計算 36
3.3.4 Finding the maximum 37
3.3.5 例子 38
第四章 研究方法步驟及結果 40
4.1研究方法 40
4.2步 驟 40
4.2.1 資料庫建立 41
4.2.2 碎形編碼(Orthonormal IFS) 43
4.2.3 使用MIL找出共有之特徵 46
4.2.4 比對方法 49
4.3 方法比較 52
4.4 實驗結果 53
第五章 結論 72
參考文獻 73
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