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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李瑞庭(Anthony J. T. Lee) | |
| dc.contributor.author | Chia-Yu Kao | en |
| dc.contributor.author | 高嘉輿 | zh_TW |
| dc.date.accessioned | 2021-06-13T01:09:43Z | - |
| dc.date.available | 2007-07-27 | |
| dc.date.copyright | 2007-07-27 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-19 | |
| dc.identifier.citation | [1]D.R. Anderson, D.J. Sweeny, T.A. Williams, Statistics for Business and Economics, Thomson South-Western, Cincinnati, OH, 2003.
[2]C.C. Chang, “Spatial match retrieval of symbolic pictures,” Journal of Information Science and Engineering, vol. 7, no. 3, pp. 405-422, 1991. [3]C.K. Chang, Q.Y. Shi and C.W. Yan, “Iconic indexing by 2-D strings,” IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 9, no. 3, pp.413-428, 1987. [4]C.C. Chang and T.C. Wu, “An exact match retrieval scheme based upon principal component analysis,” Pattern Recognition Letters, vol. 16, no. 5, pp. 465-470, 1995. [5]S.K. Chang, E. Jungert, and Y. Li, “Representation and retrieval of symbolic pictures using generalized 2D strings,” technical report, University of Pittsburg, 1988. [6]S.K. Chang, Q.Y. Shi, and C.W. Yan, “Iconic indexing by 2-D strings,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 3, pp. 413-428, 1987. [7]Y. Chen and J.Z. Wang, “A region-based fuzzy feature matching approach to content-based image retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no.9, pp. 1252-1267, 2002. [8]E.K. Essam and K. MANSUR, “A robust framework for content-based retrieval by spatial similarity in image databases”, ACM Transactions on Information Systems, vol. 17, no. 2, pp. 174-198, 1999. [9]D.S. Guru and P. Nagabhushan, “Triangular spatial relationship: a new approach for spatial knowledge representation,” Pattern Recognition Letters, vol. 22, no. 9, pp. 999-1006, 2001. [10]P.W. Huang and Y.R. Jean, “Using 2D C+-string as spatial knowledge representation for image database systems,” Pattern Recognition, vol. 27, pp. 1249-1257, 1994. [11]P.W. Huang and C.H. Lee, “Image database design based on 9D-SPA representation for spatial relation,” IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1486-1496, 2004. [12]L.J. Latecki and R. Lakamper, “Application of planar shape comparison to object retrieval in image database,” Pattern Recognition, vol. 35, pp. 15-29, 2002. [13]A.J.T. Lee and H.P. Chiu, “2D Z-string: a new spatial knowledge representation for image databases,” Pattern Recognition Letters, vol. 24, no. 16, pp. 3015-3026, 2003. [14]A.J. T. Lee, P. Yu, H.P. Chiu, and H.H. Lin, “Video algebra for spatio-temporal reasoning of iconic videos represented in 3D C-string,” Journal of Information [15]S.C. Lee, E.J. Hwang, and J.G. Han, “Efficient image retrieval based on minimal spatial relationships,” Journal of Information Science and Engineering, vol. 22, no. 2, pp. 447-459, 2006. [16]S. Y. Lee and F. J. Hsu, ”2D C-string: a new spatial knowledge representation for image database systems,” Pattern Recognition, vol. 23, no. 10, pp.1077-1087, 1990. [17]S.Y. Lee and F.J. Hsu, “Spatial reasoning and similarity retrieval of images using 2D C-string knowledge representation,” Pattern Recognition, vol. 25, no. 3, pp. 305-318, 1992. [18]A.K. Majumadar, I. Bhattacharya, and A.K. Saha, “An object-oriented fuzzy data model for similarity detection in image databases,” IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 5, pp. 1186-1189, 2002. [19]E. Petrakis, C. Faloutsos, and K.I. Lin, “ImageMap: an image indexing method based on spatial similarity,” IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 5, pp. 979-987, 2002. [20]G. Petraglia, M. Sebillo, M. Tucci, and G. Tortora, “Virtual images for similarity retrieval in image databases,” IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 6, pp. 951-967, 2001. [21]A. Rao, R.K. Srihari, L. Zhu, and A. Zhang, “A method for measuring the complexity of image databases,” IEEE Transactions on Multimedia, vol. 4, no.2, pp. 160-173, 2002. [22]E. Di Sciascio, M. Mongiello, F. M. Donini, and L. Allegretti, “Retrieval by spatial similarity: an algorithm and a comparative evaluation,” Pattern Recognition Letters, vol. 25, no. 14, pp. 1633-1645, 2004. [23]K.W. Sze, K.M. Lam, and G. Qui, “A new key frame representation for video segment retrieval,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 15, no. 9, pp. 1148-1155, 2005. [24]W.H. Yeh and Y.E. Chang, “An efficient signature extraction method for image similarity retrieval,” Journal of Information Science and Engineering, vol. 22, no. 1, pp. 63-94, 2006. [25]X.M. Zhou and C.H. Ang, “Retrieving similar pictures from a pictorial database by an improved hashing table,” Pattern Recognition Letters, vol. 18, no. 8, pp. 751-758, 1997. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29536 | - |
| dc.description.abstract | 在本篇論文中，我們提出一個新的空間關係之知識表示法「9D-SPA+」來表示一張符號圖像。植基於9D-SPA+，我們可以擷取物件間完整且正確之空間關係，同時也保留了物件的長寬與物件間距離的資訊。此外，我們針對9D-SPA+提出一個影像重構的演算法，藉此將9D-SPA+表示法轉換成符號圖像以便瀏覽。為了獲得圖像中物件間的順序與距離，我們首先將9D-SPA+表示法進行解碼，並建構一個空間關係圖形。藉由此圖形，我們可以輕易地重建出原始的符號圖像。最後，我們提出一個彈性的相似度比對演算法用以搜尋圖像資料庫，其利用物件間的空間關係和物件的長寬資訊，來評估查詢圖像與資料庫圖像的差異。藉由調整不同比對方法的權重，我們提出的方法可以滿足不同使用者的需求。最後的實驗結果證明，我們所提出的方法具有效率及擴充性，且我們所提出的多種相似度比對方法，對於不同種類的圖像皆具有高度的辨識能力。 | zh_TW |
| dc.description.abstract | In this thesis, we have proposed a new spatial knowledge structure, called 9D-SPA+, to represent symbolic images. Based on the 9D-SPA+ knowledge structure, we can capture the precise and compact spatial relations between objects and preserve the metric information of objects without ambiguity. Moreover, we propose an image reconstruction algorithm for the 9D-SPA+, which converts a 9D-SPA+ representation into a symbolic image for visualization and browsing. In order to obtain the orders and distances among objects in an image, we decode the 9D-SPA+ representation first and construct spatial relation graphs for reconstruction. Based on the graphs, we can easily reconstruct a symbolic image for a 9D-SPA+ representation. Finally, we present a flexible similarity retrieval algorithm for retrieving the similar images from the image database, which infers spatial relations among objects and metric information of objects to assess the difference between the query and database images. By adjusting the weights of different measures, our proposed method can meet users’ requirements. The experiment results show our proposed approach is scalable and efficient. Furthermore, by providing various measures for similarity retrieval, the experiments demonstrate our proposed similarity retrieval algorithm has discrimination power about different criteria. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T01:09:43Z (GMT). No. of bitstreams: 1 ntu-96-R94725003-1.pdf: 1258079 bytes, checksum: 9ef5b2129ccbfd746dacdae6b35a16d8 (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | TABLE OF CONTENTS................................i
LIST OF FIGURES.................................ii LIST OF TABLES..................................iv CHAPTER 1 INTRODUCTION...........................1 CHAPTER 2 9D-SPA+ REPRESENTATION.................5 CHAPTER 3 IMAGE RECONSTRUCTION..................10 CHAPTER 4 SIMILARITY RETRIEVAL..................20 4.1 Direction similarity measure...........21 4.2 Topology similarity measure............21 4.3 Shape similarity measure...............25 4.4 A similarity example...................28 4.5 Image retrieval algorithm..............32 CHAPTER 5 PERFORMANCE ANALYSIS..................34 5.1 Experiments on synthetic data..........34 5.2 Experiments on real data...............37 CHAPTER 6 CONCLUDING REMARKS....................45 REFERENCES......................................46 | |
| dc.language.iso | en | |
| dc.subject | 相似度比對 | zh_TW |
| dc.subject | 空間關係 | zh_TW |
| dc.subject | 圖像資料庫 | zh_TW |
| dc.subject | 9D-SPA+ | zh_TW |
| dc.subject | 知識表示法 | zh_TW |
| dc.subject | spatial relations | en |
| dc.subject | knowledge structure | en |
| dc.subject | Image database | en |
| dc.subject | 9D-SPA+ | en |
| dc.subject | similarity retrieval | en |
| dc.title | 9D-SPA+: 影像資料庫中空間關係之知識表示法 | zh_TW |
| dc.title | 9D-SPA+: A New Spatial Knowledge Representation for Image Database Systems | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 諶家蘭,吳怡瑾 | |
| dc.subject.keyword | 圖像資料庫,知識表示法,9D-SPA+,空間關係,相似度比對, | zh_TW |
| dc.subject.keyword | Image database,knowledge structure,9D-SPA+,spatial relations,similarity retrieval, | en |
| dc.relation.page | 48 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-23 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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