A quasi-random sampling approach to image retrieval

In this paper, we present a novel approach to contentsbased image retrieval. The method hinges in the use of quasi-random sampling to retrieve those images in a database which are related to a query image provided by the user. Departing from random sampling theory, we make use of the EM algorithm so as to organize the images in the database into compact clusters that can then be used for stratified random sampling. For the purposes of retrieval, we use the similarity between the query and the clustered images to govern the sampling process within clusters. In this way, the sampling can be viewed as a stratified sampling one which is random at the cluster level and takes into account the intra-cluster structure of the dataset. This approach leads to a measure of statistical confidence that relates to the theoretical hard-limit of the retrieval performance. We show results on the Oxford Flowers dataset. 1

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

Ensemble Support Vector Machine Models of Radiation-Induced Lung Injury Risk

Patients undergoing radiation therapy can develop a potentially fatal inflammation of the lungs known as radiation pneumonitis: RP). In practice, modeling RP factors is difficult because existing data are under-sampled and imbalanced. Support vector machines: SVMs), a class of statistical learning methods that implicitly maps data into a higher dimensional space, is one machine learning method that recently has been applied to the RP problem with encouraging results. In this thesis, we present and evaluate an ensemble SVM method of modeling radiation pneumonitis. The method internalizes kernel/model parameter selection into model building and enables feature scaling via Olivier Chapelle\u27s method. We show that the ensemble method provides statistically significant increases to the cross-folded area under the receiver operating characteristic curve while maintaining model parsimony. Finally, we extend our model with John C. Platt\u27s method to support non-binary outcomes in order to augment clinical relevancy

Semantic image retrieval using relevance feedback and transaction logs

Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible, and efficient means to retrieve images matching a user’s query are needed. Content-based Image Retrieval (CBIR) systems automatically extract image contents based on image features, i.e. color, texture, and shape. Relevance feedback methods are applied to CBIR to integrate users’ perceptions and reduce the gap between high-level image semantics and low-level image features. The precision of a CBIR system in retrieving semantically rich (complex) images is improved in this dissertation work by making advancements in three areas of a CBIR system: input, process, and output. The input of the system includes a mechanism that provides the user with required tools to build and modify her query through feedbacks. Users behavioral in CBIR environments are studied, and a new feedback methodology is presented to efficiently capture users’ image perceptions. The process element includes image learning and retrieval algorithms. A Long-term image retrieval algorithm (LTL), which learns image semantics from prior search results available in the system’s transaction history, is developed using Factor Analysis. Another algorithm, a short-term learner (STL) that captures user’s image perceptions based on image features and user’s feedbacks in the on-going transaction, is developed based on Linear Discriminant Analysis. Then, a mechanism is introduced to integrate these two algorithms to one retrieval procedure. Finally, a retrieval strategy that includes learning and searching phases is defined for arranging images in the output of the system. The developed relevance feedback methodology proved to reduce the effect of human subjectivity in providing feedbacks for complex images. Retrieval algorithms were applied to images with different degrees of complexity. LTL is efficient in extracting the semantics of complex images that have a history in the system. STL is suitable for query and images that can be effectively represented by their image features. Therefore, the performance of the system in retrieving images with visual and conceptual complexities was improved when both algorithms were applied simultaneously. Finally, the strategy of retrieval phases demonstrated promising results when the query complexity increases

Biased classification for relevance feedback in content-based image retrieval.

Peng, Xiang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 98-115).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.3Chapter 1.2 --- Major Contributions --- p.6Chapter 1.3 --- Thesis Outline --- p.7Chapter 2 --- Background Study --- p.9Chapter 2.1 --- Content-based Image Retrieval --- p.9Chapter 2.1.1 --- Image Representation --- p.11Chapter 2.1.2 --- High Dimensional Indexing --- p.15Chapter 2.1.3 --- Image Retrieval Systems Design --- p.16Chapter 2.2 --- Relevance Feedback --- p.19Chapter 2.2.1 --- Self-Organizing Map in Relevance Feedback --- p.20Chapter 2.2.2 --- Decision Tree in Relevance Feedback --- p.22Chapter 2.2.3 --- Bayesian Classifier in Relevance Feedback --- p.24Chapter 2.2.4 --- Nearest Neighbor Search in Relevance Feedback --- p.25Chapter 2.2.5 --- Support Vector Machines in Relevance Feedback --- p.26Chapter 2.3 --- Imbalanced Classification --- p.29Chapter 2.4 --- Active Learning --- p.31Chapter 2.4.1 --- Uncertainly-based Sampling --- p.33Chapter 2.4.2 --- Error Reduction --- p.34Chapter 2.4.3 --- Batch Selection --- p.35Chapter 2.5 --- Convex Optimization --- p.35Chapter 2.5.1 --- Overview of Convex Optimization --- p.35Chapter 2.5.2 --- Linear Program --- p.37Chapter 2.5.3 --- Quadratic Program --- p.37Chapter 2.5.4 --- Quadratically Constrained Quadratic Program --- p.37Chapter 2.5.5 --- Cone Program --- p.38Chapter 2.5.6 --- Semi-definite Program --- p.39Chapter 3 --- Imbalanced Learning with BMPM for CBIR --- p.40Chapter 3.1 --- Research Motivation --- p.41Chapter 3.2 --- Background Review --- p.42Chapter 3.2.1 --- Relevance Feedback for CBIR --- p.42Chapter 3.2.2 --- Minimax Probability Machine --- p.42Chapter 3.2.3 --- Extensions of Minimax Probability Machine --- p.44Chapter 3.3 --- Relevance Feedback using BMPM --- p.45Chapter 3.3.1 --- Model Definition --- p.45Chapter 3.3.2 --- Advantages of BMPM in Relevance Feedback --- p.46Chapter 3.3.3 --- Relevance Feedback Framework by BMPM --- p.47Chapter 3.4 --- Experimental Results --- p.47Chapter 3.4.1 --- Experiment Datasets --- p.48Chapter 3.4.2 --- Performance Evaluation --- p.50Chapter 3.4.3 --- Discussions --- p.53Chapter 3.5 --- Summary --- p.53Chapter 4 --- BMPM Active Learning for CBIR --- p.55Chapter 4.1 --- Problem Statement and Motivation --- p.55Chapter 4.2 --- Background Review --- p.57Chapter 4.3 --- Relevance Feedback by BMPM Active Learning . --- p.58Chapter 4.3.1 --- Active Learning Concept --- p.58Chapter 4.3.2 --- General Approaches for Active Learning . --- p.59Chapter 4.3.3 --- Biased Minimax Probability Machine --- p.60Chapter 4.3.4 --- Proposed Framework --- p.61Chapter 4.4 --- Experimental Results --- p.63Chapter 4.4.1 --- Experiment Setup --- p.64Chapter 4.4.2 --- Performance Evaluation --- p.66Chapter 4.5 --- Summary --- p.68Chapter 5 --- Large Scale Learning with BMPM --- p.70Chapter 5.1 --- Introduction --- p.71Chapter 5.1.1 --- Motivation --- p.71Chapter 5.1.2 --- Contribution --- p.72Chapter 5.2 --- Background Review --- p.72Chapter 5.2.1 --- Second Order Cone Program --- p.72Chapter 5.2.2 --- General Methods for Large Scale Problems --- p.73Chapter 5.2.3 --- Biased Minimax Probability Machine --- p.75Chapter 5.3 --- Efficient BMPM Training --- p.78Chapter 5.3.1 --- Proposed Strategy --- p.78Chapter 5.3.2 --- Kernelized BMPM and Its Solution --- p.81Chapter 5.4 --- Experimental Results --- p.82Chapter 5.4.1 --- Experimental Testbeds --- p.83Chapter 5.4.2 --- Experimental Settings --- p.85Chapter 5.4.3 --- Performance Evaluation --- p.87Chapter 5.5 --- Summary --- p.92Chapter 6 --- Conclusion and Future Work --- p.93Chapter 6.1 --- Conclusion --- p.93Chapter 6.2 --- Future Work --- p.94Chapter A --- List of Symbols and Notations --- p.96Chapter B --- List of Publications --- p.98Bibliography --- p.10

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

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

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a "bag of subregions". The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)