Shape-based image retrieval in iconic image databases.

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

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The challenge of abstract trademark image retrieval as a test of machine vision algorithms has attracted considerable research interest in the past decade. Current operational trademark retrieval systems involve manual annotation of the images (the current ‘gold standard’). Accordingly, current systems require a substantial amount of time and labour to access, and are therefore expensive to operate. This thesis focuses on the development of algorithms that mimic aspects of human visual perception in order to retrieve similar abstract trademark images automatically. A significant category of trademark images are typically highly stylised, comprising a collection of distinctive graphical elements that often include geometric shapes. Therefore, in order to compare the similarity of such images the principal aim of this research has been to develop a method for solving the partial matching and shape perception problem. There are few useful techniques for partial shape matching in the context of trademark retrieval, because those existing techniques tend not to support multicomponent retrieval. When this work was initiated most trademark image retrieval systems represented images by means of global features, which are not suited to solving the partial matching problem. Instead, the author has investigated the use of local image features as a means to finding similarities between trademark images that only partially match in terms of their subcomponents. During the course of this work, it has been established that the Harris and Chabat detectors could potentially perform sufficiently well to serve as the basis for local feature extraction in trademark image retrieval. Early findings in this investigation indicated that the well established SIFT (Scale Invariant Feature Transform) local features, based on the Harris detector, could potentially serve as an adequate underlying local representation for matching trademark images. There are few researchers who have used mechanisms based on human perception for trademark image retrieval, implying that the shape representations utilised in the past to solve this problem do not necessarily reflect the shapes contained in these image, as characterised by human perception. In response, a ii practical approach to trademark image retrieval by perceptual grouping has been developed based on defining meta-features that are calculated from the spatial configurations of SIFT local image features. This new technique measures certain visual properties of the appearance of images containing multiple graphical elements and supports perceptual grouping by exploiting the non-accidental properties of their configuration. Our validation experiments indicated that we were indeed able to capture and quantify the differences in the global arrangement of sub-components evident when comparing stylised images in terms of their visual appearance properties. Such visual appearance properties, measured using 17 of the proposed metafeatures, include relative sub-component proximity, similarity, rotation and symmetry. Similar work on meta-features, based on the above Gestalt proximity, similarity, and simplicity groupings of local features, had not been reported in the current computer vision literature at the time of undertaking this work. We decided to adopted relevance feedback to allow the visual appearance properties of relevant and non-relevant images returned in response to a query to be determined by example. Since limited training data is available when constructing a relevance classifier by means of user supplied relevance feedback, the intrinsically non-parametric machine learning algorithm ID3 (Iterative Dichotomiser 3) was selected to construct decision trees by means of dynamic rule induction. We believe that the above approach to capturing high-level visual concepts, encoded by means of meta-features specified by example through relevance feedback and decision tree classification, to support flexible trademark image retrieval and to be wholly novel. The retrieval performance the above system was compared with two other state-of-the-art image trademark retrieval systems: Artisan developed by Eakins (Eakins et al., 1998) and a system developed by Jiang (Jiang et al., 2006). Using relevance feedback, our system achieves higher average normalised precision than either of the systems developed by Eakins’ or Jiang. However, while our trademark image query and database set is based on an image dataset used by Eakins, we employed different numbers of images. It was not possible to access to the same query set and image database used in the evaluation of Jiang’s trademark iii image retrieval system evaluation. Despite these differences in evaluation methodology, our approach would appear to have the potential to improve retrieval effectiveness

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Maximal disk based histogram for shape retrieval

2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe