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dc.contributorMark Liaoen_US
dc.contributorC C. Changen_US
dc.contributorWen-Kuang Chouen_US
dc.contributorYen-Ching Changen_US
dc.contributorLin-Yu Tsengen_US
dc.contributorGwoboa Horngen_US
dc.contributorC. M. Wangen_US
dc.contributor.advisorPo-Whei Huangen_US
dc.contributor.authorHsu, Lipinen_US
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dc.description.abstract在本篇論文中,我們提出兩個影像表示法來幫助建立CBIR (Content-Based Image Retrieval)系統。第一個是以物件形狀為主的影像表示法,稱為NOAR (Normalized Object Area Representation),第二個是以空間關係為主,稱為Extended 9D-SPA (9-Direction SPanning Area)影像表示法。NOAR能精確地表示物件之間的空間關係且提供精細的視覺化及有效的空間推理功能。以NOAR表示法為基礎,我們進而提出可用於評估任兩張影像間相似度的衡量尺度方法,並以一些例子來驗證NOAR影像表示法的有效性。Extended 9D-SPA影像表示法係延伸之前所提出的9D-SPA表示法,具有更精確的影像區別能力及彈性度。此方法可提供不同程度的空間推理,在影像擷取方面則提出十二種影像相似度的比對標準,以不同的彈性度來滿足使用者之各種需求。在3600張圖形資料庫實驗系統中,系統可以同時達到86.1%的準確率(precision)及81.2%的召回度(recall)。zh_TW
dc.description.abstractIn this dissertation, two image representation methods for Content-Based Image Retrieval are proposed. The first image representation method is called NOAR (Normalized Object Area Representation), which can capture the shapes and locations, as well as orientations of objects in an image. Image information retrieval systems with important functions such as spatial reasoning, visualization, browsing, and similarity retrieval can be easily built based on NOAR. The effectiveness of NOAR was demonstrated by several examples. The second image representation method is called Extended 9D-SPA, which is an extension of 9D-SPA. This method can provide different degrees of granularity for reasoning about directional relations in both eight- and sixteen-direction reference frames. In similarity retrieval, it provides twelve types of similarity measures to support flexible matching between the query picture and the database pictures. By exercising a database containing 3600 pictures, we successfully demonstrated the effectiveness of our image retrieval system based on Extended 9D-SPA. Experiment result showed that 86.1% precision rate and 81.2% recall rate can be achieved simultaneously. This performance is considered to be very good as an effective information retrieval system.en_US
dc.description.tableofcontents1 Introduction 1 1.1 Problem Descriptions 1 1.2 General Objective and Approaches 4 1.3 Organization of the Dissertation 5 2 Background 6 2.1 Content-Based Image Retrieval Systems 6 2.2 Shape Description 8 2.3 Spatial Relation Description 11 2.4 Image Indexing 20 2.5 Spatial Reasoning 24 2.6 Similarity Retrieval 28 2.7 Summary 31 3 Shape-Based Image Representation: NOAR 32 3.1 The NOAR Image Representation Method 32 3.2 Visualization and Picture Reconstruction 35 3.3 Spatial Reasoning Based on NOAR 41 3.3.1 Directional Relationship Inference 41 3.3.2 Topological Relationship Inference 42 3.4 Summary 47 4 Similarity Retrieval Based on NOAR 50 4.1 Similarity Measurement by NOAR 50 4.2 Retrieving Images Indexed by NOAR 52 4.3 Examples 53 4.4 Summary 54 5 Relation-Based Image Representation: Extended 9D-SPA 57 5.1 The Extended 9D-SPA Representation Method 58 5.2 Examples and Discussion 62 5.3 Spatial Reasoning Based on Extended 9D-SPA 67 5.4 Summary 71 6 Similarity Retrieval Based on Extended 9D-SPA 74 6.1 Similarity Measurement by Extended 9D-SPA 74 6.2 Examples 79 6.3 Retrieving Images Indexed by Extended 9D-SPA 83 6.4 Experiment Results 83 6.5 Summary 86 7 Conclusions 89 7.1 Key Results 89 7.2 Suggestions for Future Research Direction 91 Bibliography 92 VITA 103 Publications 104en_US
dc.subjectspatial relationen_US
dc.subjectimage databaseen_US
dc.titleImage Information Retrieval Based on Shapes and Spatial Relations for Pictures Containing Sized Objectsen_US
dc.typeThesis and Dissertationzh_TW
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item.openairetypeThesis and Dissertation-
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