Yip King-Fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 84-89). / Abstracts in English and Chinese. / Abstract --- p.i / 論文摘要 --- p.iii / Table of Contents --- p.iv / List of Abbreviations --- p.vi / Acknowledgements --- p.vii / Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Background --- p.1 / Chapter 1.2. --- Motivation --- p.3 / Chapter 1.3. --- Thesis Organization --- p.4 / Chapter 2. --- A Survey of Content-based Image Retrieval --- p.5 / Chapter 2.1. --- Text-based Image Retrieval --- p.5 / Chapter 2.2. --- Content-Based Image Retrieval --- p.7 / Chapter 2.2.1. --- Content-Based Image Retrieval Systems --- p.7 / Chapter 2.2.2. --- Query Methods --- p.9 / Chapter 2.2.3. --- Image Features --- p.11 / Chapter 2.2.4. --- Summary --- p.16 / Chapter 3. --- Visual Thesaurus using SOM --- p.17 / Chapter 3.1. --- Algorithm --- p.17 / Chapter 3.1.1. --- Image Representation --- p.17 / Chapter 3.1.2. --- Self-Organizing Map --- p.21 / Chapter 3.2. --- Preliminary Experiment --- p.27 / Chapter 3.2.1. --- Feature differences --- p.27 / Chapter 3.2.2. --- Labeling differences --- p.30 / Chapter 4. --- Experiment --- p.33 / Chapter 4.1. --- Subjects --- p.33 / Chapter 4.2. --- Apparatus --- p.33 / Chapter 4.2.1. --- Systems --- p.33 / Chapter 4.2.2. --- Test Databases --- p.33 / Chapter 4.3. --- Procedure --- p.34 / Chapter 4.3.1. --- Description --- p.35 / Chapter 4.3.2. --- SOM (text) --- p.36 / Chapter 4.3.3. --- SOM (image) --- p.38 / Chapter 4.3.4. --- QBE (text) --- p.40 / Chapter 4.3.5. --- QBE (image) --- p.42 / Chapter 4.3.6. --- Questionnaire --- p.44 / Chapter 4.3.7. --- Experiment Flow --- p.45 / Chapter 4.4. --- Results --- p.46 / Chapter 4.5. --- Discussion --- p.51 / Chapter 5. --- Quantizing Color Histogram --- p.55 / Chapter 5.1. --- Algorithm --- p.56 / Chapter 5.1.1. --- Codebook Generation Phrase --- p.57 / Chapter 5.1.2. --- Histogram Generation Phrase --- p.66 / Chapter 5.2. --- Experiment --- p.67 / Chapter 5.2.1. --- Test Database --- p.67 / Chapter 5.2.2. --- Evaluation Methods --- p.67 / Chapter 5.2.3. --- Results and Discussion --- p.69 / Chapter 5.2.4. --- Summary --- p.74 / Chapter 6. --- Relevance Feedback --- p.75 / Chapter 6.1. --- Relevance Feedback in Text Information Retrieval --- p.75 / Chapter 6.2. --- Relevance Feedback in Multimedia Information Retrieval --- p.76 / Chapter 6.3. --- Relevance Feedback in Visual Thesaurus --- p.76 / Chapter 7. --- Conclusions --- p.80 / Chapter 7.1. --- Applications --- p.81 / Chapter 7.2. --- Future Directions --- p.81 / Chapter 7.2.1. --- SOM Generation --- p.81 / Chapter 7.2.2. --- Hybrid Architecture --- p.82 / References --- p.84

Abstract not available

Thesis (Ph.D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 88 pages. Includes vita. Includes bibliographical references.

The creation, storage, manipulation, and transmission of images have become less costly and more efficient. Consequently, the numbers of images and their users are growing rapidly. This poses challenges to those who organize and provide access to them. One of these challenges is similarity matching. Most current content-based image retrieval (CBIR) systems which can extract only low-level visual features such as color, shape, and texture, use similarity measures based on geometric models of similarity. However, most human similarity judgment data violate the metric axioms of these models. Tversky's (1977) contrast model, which defines similarity as a feature contrast task and equates the degree of similarity of two stimuli to a linear combination of their common and distinctive features, explains human similarity judgments much better than the geometric models. This study tested the contrast model as a conceptual framework to investigate the nature of the relationships between features and similarity of images as perceived by human judges. Data were collected from 150 participants who performed two tasks: an image description and a similarity judgment task. Qualitative methods (content analysis) and quantitative (correlational) methods were used to seek answers to four research questions related to the relationships between common and distinctive features and similarity judgments of images as well as measures of their common and distinctive features. Structural equation modeling, correlation analysis, and regression analysis confirmed the relationships between perceived features and similarity of objects hypothesized by Tversky (1977). Tversky's (1977) contrast model based upon a combination of two methods for measuring common and distinctive features, and two methods for measuring similarity produced statistically significant structural coefficients between the independent latent variables (common and distinctive features) and the dependent latent variable (similarity). This model fit the data well for a sample of 30 (435 pairs of) images and 150 participants (χ2 =16.97, df=10, p = .07508, RMSEA= .040, SRMR= .0205, GFI= .990, AGFI= .965). The goodness of fit indices showed the model did not significantly deviate from the actual sample data. This study is the first to test the contrast model in the context of information representation and retrieval. Results of the study are hoped to provide the foundations for future research that will attempt to further test the contrast model and assist designers of image organization and retrieval systems by pointing toward alternative document representations and similarity measures that more closely match human similarity judgments.

The purpose of dissertation is to utilize connotative messages for enhancing image retrieval and browsing. By adopting semiotics as a theoretical tool, this study explores problems of image retrieval and proposes an image retrieval model. The semiotics approach conceptually demonstrates that: 1) a fundamental reason for the dissonance between retrieved images and user needs is representation of connotative messages, and 2) the image retrieval model which makes use of denotative index terms is able to facilitate users to browse connotatively related images effectively even when the users' needs are potentially expressed in the form of denotative query. Two experiments are performed for verifying the semiotic-based image retrieval model and evaluating the effectiveness of the model. As data sources, 5,199 records are collected from Artefacts Canada: Humanities by Canadian Heritage Information Network, and the candidate terms of connotation and denotation are extracted from Art & Architecture Thesaurus. The first experiment, by applying term association measures, verifies that the connotative messages of an image can be derived from denotative messages of the image. The second experiment reveals that the association thesaurus which is constructed based on the associations between connotation and denotation facilitates assigning connotative terms to image documents. In addition, the result of relevant judgments presents that the association thesaurus improves the relative recall of retrieved image documents as well as the relative recall of browsing sets. This study concludes that the association thesaurus indicating associations between connotation and denotation is able to improve the accessibility of the connotative messages. The results of the study are hoped to contribute to the conceptual knowledge of image retrieval by providing understandings of connotative messages within an image and to the practical design of image retrieval system by proposing an association thesaurus which can supplement the limitations of the current content-based image retrieval systems (CBIR).

No description available.

by Chan Yuk Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 117-124). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Content-based Image Retrieval --- p.3 / Chapter 1.2 --- Designing a Shape-based Image Retrieval System --- p.4 / Chapter 1.3 --- Information on Trademark --- p.6 / Chapter 1.3.1 --- What is a Trademark? --- p.6 / Chapter 1.3.2 --- Search for Conflicting Trademarks --- p.7 / Chapter 1.3.3 --- Research Scope --- p.8 / Chapter 1.4 --- Information on Chinese Cursive Script Character --- p.9 / Chapter 1.5 --- Problem Definition --- p.9 / Chapter 1.6 --- Contributions --- p.11 / Chapter 1.7 --- Thesis Organization --- p.13 / Chapter 2 --- Literature Review --- p.14 / Chapter 2.1 --- Trademark Retrieval using QBIC Technology --- p.14 / Chapter 2.2 --- STAR --- p.16 / Chapter 2.3 --- ARTISAN --- p.17 / Chapter 2.4 --- Trademark Retrieval using a Visually Salient Feature --- p.18 / Chapter 2.5 --- Trademark Recognition using Closed Contours --- p.19 / Chapter 2.6 --- Trademark Retrieval using a Two Stage Hierarchy --- p.19 / Chapter 2.7 --- Logo Matching using Negative Shape Features --- p.21 / Chapter 2.8 --- Chapter Summary --- p.22 / Chapter 3 --- Background on Shape Representation and Matching --- p.24 / Chapter 3.1 --- Simple Geometric Features --- p.25 / Chapter 3.1.1 --- Circularity --- p.25 / Chapter 3.1.2 --- Rectangularity --- p.26 / Chapter 3.1.3 --- Hole Area Ratio --- p.27 / Chapter 3.1.4 --- Horizontal Gap Ratio --- p.27 / Chapter 3.1.5 --- Vertical Gap Ratio --- p.28 / Chapter 3.1.6 --- Central Moments --- p.28 / Chapter 3.1.7 --- Major Axis Orientation --- p.29 / Chapter 3.1.8 --- Eccentricity --- p.30 / Chapter 3.2 --- Fourier Descriptors --- p.30 / Chapter 3.3 --- Chain Codes --- p.31 / Chapter 3.4 --- Seven Invariant Moments --- p.33 / Chapter 3.5 --- Zernike Moments --- p.35 / Chapter 3.6 --- Edge Direction Histogram --- p.36 / Chapter 3.7 --- Curvature Scale Space Representation --- p.37 / Chapter 3.8 --- Chapter Summary --- p.39 / Chapter 4 --- Genetic Algorithm for Weight Assignment --- p.42 / Chapter 4.1 --- Genetic Algorithm (GA) --- p.42 / Chapter 4.1.1 --- Basic Idea --- p.43 / Chapter 4.1.2 --- Genetic Operators --- p.44 / Chapter 4.2 --- Why GA? --- p.45 / Chapter 4.3 --- Weight Assignment Problem --- p.46 / Chapter 4.3.1 --- Integration of Image Attributes --- p.46 / Chapter 4.4 --- Proposed Solution --- p.47 / Chapter 4.4.1 --- Formalization --- p.47 / Chapter 4.4.2 --- Proposed Genetic Algorithm --- p.43 / Chapter 4.5 --- Chapter Summary --- p.49 / Chapter 5 --- Shape-based Trademark Image Retrieval System --- p.50 / Chapter 5.1 --- Problems on Existing Methods --- p.50 / Chapter 5.1.1 --- Edge Direction Histogram --- p.51 / Chapter 5.1.2 --- Boundary Based Techniques --- p.52 / Chapter 5.2 --- Proposed Solution --- p.53 / Chapter 5.2.1 --- Image Preprocessing --- p.53 / Chapter 5.2.2 --- Automatic Feature Extraction --- p.54 / Chapter 5.2.3 --- Approximated Boundary --- p.55 / Chapter 5.2.4 --- Integration of Shape Features and Query Processing --- p.58 / Chapter 5.3 --- Experimental Results --- p.58 / Chapter 5.3.1 --- Experiment 1: Weight Assignment using Genetic Algorithm --- p.59 / Chapter 5.3.2 --- Experiment 2: Speed on Feature Extraction and Retrieval --- p.62 / Chapter 5.3.3 --- Experiment 3: Evaluation by Precision --- p.63 / Chapter 5.3.4 --- Experiment 4: Evaluation by Recall for Deformed Images --- p.64 / Chapter 5.3.5 --- Experiment 5: Evaluation by Recall for Hand Drawn Query Trademarks --- p.66 / Chapter 5.3.6 --- "Experiment 6: Evaluation by Recall for Rotated, Scaled and Mirrored Images" --- p.66 / Chapter 5.3.7 --- Experiment 7: Comparison of Different Integration Methods --- p.68 / Chapter 5.4 --- Chapter Summary --- p.71 / Chapter 6 --- Shape-based Chinese Cursive Script Character Image Retrieval System --- p.72 / Chapter 6.1 --- Comparison to Trademark Retrieval Problem --- p.79 / Chapter 6.1.1 --- Feature Selection --- p.73 / Chapter 6.1.2 --- Speed of System --- p.73 / Chapter 6.1.3 --- Variation of Style --- p.73 / Chapter 6.2 --- Target of the Research --- p.74 / Chapter 6.3 --- Proposed Solution --- p.75 / Chapter 6.3.1 --- Image Preprocessing --- p.75 / Chapter 6.3.2 --- Automatic Feature Extraction --- p.76 / Chapter 6.3.3 --- Thinned Image and Linearly Normalized Image --- p.76 / Chapter 6.3.4 --- Edge Directions --- p.77 / Chapter 6.3.5 --- Integration of Shape Features --- p.78 / Chapter 6.4 --- Experimental Results --- p.79 / Chapter 6.4.1 --- Experiment 8: Weight Assignment using Genetic Algorithm --- p.79 / Chapter 6.4.2 --- Experiment 9: Speed on Feature Extraction and Retrieval --- p.81 / Chapter 6.4.3 --- Experiment 10: Evaluation by Recall for Deformed Images --- p.82 / Chapter 6.4.4 --- Experiment 11: Evaluation by Recall for Rotated and Scaled Images --- p.83 / Chapter 6.4.5 --- Experiment 12: Comparison of Different Integration Methods --- p.85 / Chapter 6.5 --- Chapter Summary --- p.87 / Chapter 7 --- Conclusion --- p.88 / Chapter 7.1 --- Summary --- p.88 / Chapter 7.2 --- Future Research --- p.89 / Chapter 7.2.1 --- Limitations --- p.89 / Chapter 7.2.2 --- Future Directions --- p.90 / Chapter A --- A Representative Subset of Trademark Images --- p.91 / Chapter B --- A Representative Subset of Cursive Script Character Images --- p.93 / Chapter C --- Shape Feature Extraction Toolbox for Matlab V53 --- p.95 / Chapter C.l --- central .moment --- p.95 / Chapter C.2 --- centroid --- p.96 / Chapter C.3 --- cir --- p.96 / Chapter C.4 --- ess --- p.97 / Chapter C.5 --- css_match --- p.100 / Chapter C.6 --- ecc --- p.102 / Chapter C.7 --- edge一directions --- p.102 / Chapter C.8 --- fourier-d --- p.105 / Chapter C.9 --- gen_shape --- p.106 / Chapter C.10 --- hu7 --- p.108 / Chapter C.11 --- isclockwise --- p.109 / Chapter C.12 --- moment --- p.110 / Chapter C.13 --- normalized-moment --- p.111 / Chapter C.14 --- orientation --- p.111 / Chapter C.15 --- resample-pts --- p.112 / Chapter C.16 --- rectangularity --- p.113 / Chapter C.17 --- trace-points --- p.114 / Chapter C.18 --- warp-conv --- p.115 / Bibliography --- p.117

Gong, Boqing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 53-59). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- 3D Object Representation --- p.1 / Chapter 1.1.1 --- Polygon Mesh --- p.2 / Chapter 1.1.2 --- Voxel --- p.2 / Chapter 1.1.3 --- Range Image --- p.3 / Chapter 1.2 --- Content-Based 3D Object Retrieval --- p.3 / Chapter 1.3 --- 3D Facial Expression Recognition --- p.4 / Chapter 1.4 --- Contributions --- p.5 / Chapter 2 --- 3D Object Retrieval --- p.6 / Chapter 2.1 --- A Conceptual Framework for 3D Object Retrieval --- p.6 / Chapter 2.1.1 --- Query Formulation and User Interface --- p.7 / Chapter 2.1.2 --- Canonical Coordinate Normalization --- p.8 / Chapter 2.1.3 --- Representations of 3D Objects --- p.10 / Chapter 2.1.4 --- Performance Evaluation --- p.11 / Chapter 2.2 --- Public Databases --- p.13 / Chapter 2.2.1 --- Databases of Generic 3D Objects --- p.14 / Chapter 2.2.2 --- A Database of Articulated Objects --- p.15 / Chapter 2.2.3 --- Domain-Specific Databases --- p.15 / Chapter 2.2.4 --- Data Sets for the Shrec Contest --- p.16 / Chapter 2.3 --- Experimental Systems --- p.16 / Chapter 2.4 --- Challenges in 3D Object Retrieval --- p.17 / Chapter 3 --- Boosting 3D Object Retrieval by Object Flexibility --- p.19 / Chapter 3.1 --- Related Work --- p.19 / Chapter 3.2 --- Object Flexibility --- p.21 / Chapter 3.2.1 --- Definition --- p.21 / Chapter 3.2.2 --- Computation of the Flexibility --- p.22 / Chapter 3.3 --- A Flexibility Descriptor for 3D Object Retrieval --- p.24 / Chapter 3.4 --- Enhancing Existing Methods --- p.25 / Chapter 3.5 --- Experiments --- p.26 / Chapter 3.5.1 --- Retrieving Articulated Objects --- p.26 / Chapter 3.5.2 --- Retrieving Generic Objects --- p.27 / Chapter 3.5.3 --- Experiments on Larger Databases --- p.28 / Chapter 3.5.4 --- Comparison of Times for Feature Extraction --- p.31 / Chapter 3.6 --- Conclusions & Analysis --- p.31 / Chapter 4 --- 3D Object Retrieval with Referent Objects --- p.32 / Chapter 4.1 --- 3D Object Retrieval with Prior --- p.32 / Chapter 4.2 --- 3D Object Retrieval with Referent Objects --- p.34 / Chapter 4.2.1 --- Natural and Man-made 3D Object Classification --- p.35 / Chapter 4.2.2 --- Inferring Priors Using 3D Object Classifier --- p.36 / Chapter 4.2.3 --- Reducing False Positives --- p.37 / Chapter 4.3 --- Conclusions and Future Work --- p.38 / Chapter 5 --- 3D Facial Expression Recognition --- p.39 / Chapter 5.1 --- Introduction --- p.39 / Chapter 5.2 --- Separation of BFSC and ESC --- p.43 / Chapter 5.2.1 --- 3D Face Alignment --- p.43 / Chapter 5.2.2 --- Estimation of BFSC --- p.44 / Chapter 5.3 --- Expressional Regions and an Expression Descriptor --- p.45 / Chapter 5.4 --- Experiments --- p.47 / Chapter 5.4.1 --- Testing the Ratio of Preserved Energy in the BFSC Estimation --- p.47 / Chapter 5.4.2 --- Comparison with Related Work --- p.48 / Chapter 5.5 --- Conclusions --- p.50 / Chapter 6 --- Conclusions --- p.51 / Bibliography --- p.53

This study investigates the relationships between the use of a zoom tool, the terms they supply to describe the image, and the type of image being viewed. Participants were assigned to two groups, one with access to the tool and one without, and were asked to supply terms to describe forty images, divided into four categories: landscape, portrait, news, and cityscape. The terms provided by participants were categorized according to models proposed in earlier image studies. Findings of the study suggest that there was not a significant difference in the number of terms supplied in relation to access to the tool, but a large variety in use of the tool was demonstrated by the participants. The study shows that there are differences in the level of meaning of the terms supplied in some of the models. The type of image being viewed was related to the number of zooms and relationships between the type of image and the number of terms supplied as well as their level of meaning in the various models from previous studies exist. The results of this study provide further insight into how people think about images and how the manipulation of those images may affect the terms they assign to describe images. The inclusion of these tools in search and retrieval scenarios may affect the outcome of the process and the more collection managers know about how people interact with images will improve their ability to provide access to the growing amount of pictorial information.

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