Supervised Content based Image Retrieval using Fuzzy Texton and Shearlet Transform

In this paper we proposed, a novel framework to assist and automate the diagnosis of diseases from computer-based image analysis method using Content-based image retrieval (CBIR). CBIR is the process of retrieving related images from large database collections by using low level image features such as color, texture and shape etc. we have used fuzzy texton and discrete shearlet transform to extract texture and shape features. The aim is to support decision making by retrieving and displaying relevant past cases visually similar to the one under examination with relevance feedback using Support Vector Machines

Learning on relevance feedback in content-based image retrieval.

Hoi, Chu-Hong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 89-103).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Content-based Image Retrieval --- p.1Chapter 1.2 --- Relevance Feedback --- p.3Chapter 1.3 --- Contributions --- p.4Chapter 1.4 --- Organization of This Work --- p.6Chapter 2 --- Background --- p.8Chapter 2.1 --- Relevance Feedback --- p.8Chapter 2.1.1 --- Heuristic Weighting Methods --- p.9Chapter 2.1.2 --- Optimization Formulations --- p.10Chapter 2.1.3 --- Various Machine Learning Techniques --- p.11Chapter 2.2 --- Support Vector Machines --- p.12Chapter 2.2.1 --- Setting of the Learning Problem --- p.12Chapter 2.2.2 --- Optimal Separating Hyperplane --- p.13Chapter 2.2.3 --- Soft-Margin Support Vector Machine --- p.15Chapter 2.2.4 --- One-Class Support Vector Machine --- p.16Chapter 3 --- Relevance Feedback with Biased SVM --- p.18Chapter 3.1 --- Introduction --- p.18Chapter 3.2 --- Biased Support Vector Machine --- p.19Chapter 3.3 --- Relevance Feedback Using Biased SVM --- p.22Chapter 3.3.1 --- Advantages of BSVM in Relevance Feedback --- p.22Chapter 3.3.2 --- Relevance Feedback Algorithm by BSVM --- p.23Chapter 3.4 --- Experiments --- p.24Chapter 3.4.1 --- Datasets --- p.24Chapter 3.4.2 --- Image Representation --- p.25Chapter 3.4.3 --- Experimental Results --- p.26Chapter 3.5 --- Discussions --- p.29Chapter 3.6 --- Summary --- p.30Chapter 4 --- Optimizing Learning with SVM Constraint --- p.31Chapter 4.1 --- Introduction --- p.31Chapter 4.2 --- Related Work and Motivation --- p.33Chapter 4.3 --- Optimizing Learning with SVM Constraint --- p.35Chapter 4.3.1 --- Problem Formulation and Notations --- p.35Chapter 4.3.2 --- Learning boundaries with SVM --- p.35Chapter 4.3.3 --- OPL for the Optimal Distance Function --- p.38Chapter 4.3.4 --- Overall Similarity Measure with OPL and SVM --- p.40Chapter 4.4 --- Experiments --- p.41Chapter 4.4.1 --- Datasets --- p.41Chapter 4.4.2 --- Image Representation --- p.42Chapter 4.4.3 --- Performance Evaluation --- p.43Chapter 4.4.4 --- Complexity and Time Cost Evaluation --- p.45Chapter 4.5 --- Discussions --- p.47Chapter 4.6 --- Summary --- p.48Chapter 5 --- Group-based Relevance Feedback --- p.49Chapter 5.1 --- Introduction --- p.49Chapter 5.2 --- SVM Ensembles --- p.50Chapter 5.3 --- Group-based Relevance Feedback Using SVM Ensembles --- p.51Chapter 5.3.1 --- (x+l)-class Assumption --- p.51Chapter 5.3.2 --- Proposed Architecture --- p.52Chapter 5.3.3 --- Strategy for SVM Combination and Group Ag- gregation --- p.52Chapter 5.4 --- Experiments --- p.54Chapter 5.4.1 --- Experimental Implementation --- p.54Chapter 5.4.2 --- Performance Evaluation --- p.55Chapter 5.5 --- Discussions --- p.56Chapter 5.6 --- Summary --- p.57Chapter 6 --- Log-based Relevance Feedback --- p.58Chapter 6.1 --- Introduction --- p.58Chapter 6.2 --- Related Work and Motivation --- p.60Chapter 6.3 --- Log-based Relevance Feedback Using SLSVM --- p.61Chapter 6.3.1 --- Problem Statement --- p.61Chapter 6.3.2 --- Soft Label Support Vector Machine --- p.62Chapter 6.3.3 --- LRF Algorithm by SLSVM --- p.64Chapter 6.4 --- Experimental Results --- p.66Chapter 6.4.1 --- Datasets --- p.66Chapter 6.4.2 --- Image Representation --- p.66Chapter 6.4.3 --- Experimental Setup --- p.67Chapter 6.4.4 --- Performance Comparison --- p.68Chapter 6.5 --- Discussions --- p.73Chapter 6.6 --- Summary --- p.75Chapter 7 --- Application: Web Image Learning --- p.76Chapter 7.1 --- Introduction --- p.76Chapter 7.2 --- A Learning Scheme for Searching Semantic Concepts --- p.77Chapter 7.2.1 --- Searching and Clustering Web Images --- p.78Chapter 7.2.2 --- Learning Semantic Concepts with Relevance Feed- back --- p.73Chapter 7.3 --- Experimental Results --- p.79Chapter 7.3.1 --- Dataset and Features --- p.79Chapter 7.3.2 --- Performance Evaluation --- p.80Chapter 7.4 --- Discussions --- p.82Chapter 7.5 --- Summary --- p.82Chapter 8 --- Conclusions and Future Work --- p.84Chapter 8.1 --- Conclusions --- p.84Chapter 8.2 --- Future Work --- p.85Chapter A --- List of Publications --- p.87Bibliography --- p.10

Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance

Multitraining support vector machine for image retrieval

Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small. This is mainly due to 1) the SVM classifier being unstable for small-size training sets because its optimal hyper plane is too sensitive to the training examples; and 2) the kernel method being ineffective because the feature dimension is much greater than the size of the training samples. In this paper, we develop a new machine learning technique, multitraining SVM (MTSVM), which combines the merits of the cotraining technique and a random sampling method in the feature space. Based on the proposed MTSVM algorithm, the above two problems can be mitigated. Experiments are carried out on a large image set of some 20 000 images, and the preliminary results demonstrate that the developed method consistently improves the performance over conventional SVM-based RFs in terms of precision and standard deviation, which are used to evaluate the effectiveness and robustness of a RF algorithm, respectively

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Interactive retrieval of video using pre-computed shot-shot similarities

A probabilistic framework for content-based interactive video retrieval is described. The developed indexing of video fragments originates from the probability of the user's positive judgment about key-frames of video shots. Initial estimates of the probabilities are obtained from low-level feature representation. Only statistically significant estimates are picked out, the rest are replaced by an appropriate constant allowing efficient access at search time without loss of search quality and leading to improvement in most experiments. With time, these probability estimates are updated from the relevance judgment of users performing searches, resulting in further substantial increases in mean average precision