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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Content-based image retrieval-- a small sample learning approach.

January 2004 (has links)
Tao Dacheng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 70-75). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Content-based Image Retrieval --- p.1 / Chapter 1.2 --- SVM based RF in CBIR --- p.3 / Chapter 1.3 --- DA based RF in CBIR --- p.4 / Chapter 1.4 --- Existing CBIR Engines --- p.5 / Chapter 1.5 --- Practical Applications of CBIR --- p.10 / Chapter 1.6 --- Organization of this thesis --- p.11 / Chapter Chapter 2 --- Statistical Learning Theory and Support Vector Machine --- p.12 / Chapter 2.1 --- The Recognition Problem --- p.12 / Chapter 2.2 --- Regularization --- p.14 / Chapter 2.3 --- The VC Dimension --- p.14 / Chapter 2.4 --- Structure Risk Minimization --- p.15 / Chapter 2.5 --- Support Vector Machine --- p.15 / Chapter 2.6 --- Kernel Space --- p.17 / Chapter Chapter 3 --- Discriminant Analysis --- p.18 / Chapter 3.1 --- PCA --- p.18 / Chapter 3.2 --- KPCA --- p.18 / Chapter 3.3 --- LDA --- p.20 / Chapter 3.4 --- BDA --- p.20 / Chapter 3.5 --- KBDA --- p.21 / Chapter Chapter 4 --- Random Sampling Based SVM --- p.24 / Chapter 4.1 --- Asymmetric Bagging SVM --- p.25 / Chapter 4.2 --- Random Subspace Method SVM --- p.26 / Chapter 4.3 --- Asymmetric Bagging RSM SVM --- p.26 / Chapter 4.4 --- Aggregation Model --- p.30 / Chapter 4.5 --- Dissimilarity Measure --- p.31 / Chapter 4.6 --- Computational Complexity Analysis --- p.31 / Chapter 4.7 --- QueryGo Image Retrieval System --- p.32 / Chapter 4.8 --- Toy Experiments --- p.35 / Chapter 4.9 --- Statistical Experimental Results --- p.36 / Chapter Chapter 5 --- SSS Problems in KBDA RF --- p.42 / Chapter 5.1 --- DKBDA --- p.43 / Chapter 5.1.1 --- DLDA --- p.43 / Chapter 5.1.2 --- DKBDA --- p.43 / Chapter 5.2 --- NKBDA --- p.48 / Chapter 5.2.1 --- NLDA --- p.48 / Chapter 5.2.2 --- NKBDA --- p.48 / Chapter 5.3 --- FKBDA --- p.49 / Chapter 5.3.1 --- FLDA --- p.49 / Chapter 5.3.2 --- FKBDA --- p.49 / Chapter 5.4 --- Experimental Results --- p.50 / Chapter Chapter 6 --- NDA based RF for CBIR --- p.52 / Chapter 6.1 --- NDA --- p.52 / Chapter 6.2 --- SSS Problem in NDA --- p.53 / Chapter 6.2.1 --- Regularization method --- p.53 / Chapter 6.2.2 --- Null-space method --- p.54 / Chapter 6.2.3 --- Full-space method --- p.54 / Chapter 6.3 --- Experimental results --- p.55 / Chapter 6.3.1 --- K nearest neighbor evaluation for NDA --- p.55 / Chapter 6.3.2 --- SSS problem --- p.56 / Chapter 6.3.3 --- Evaluation experiments --- p.57 / Chapter Chapter 7 --- Medical Image Classification --- p.59 / Chapter 7.1 --- Introduction --- p.59 / Chapter 7.2 --- Region-based Co-occurrence Matrix Texture Feature --- p.60 / Chapter 7.3 --- Multi-level Feature Selection --- p.62 / Chapter 7.4 --- Experimental Results --- p.63 / Chapter 7.4.1 --- Data Set --- p.64 / Chapter 7.4.2 --- Classification Using Traditional Features --- p.65 / Chapter 7.4.3 --- Classification Using the New Features --- p.66 / Chapter Chapter 8 --- Conclusion --- p.68 / Bibliography --- p.70

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