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

TagBook: A Semantic Video Representation without Supervision for Event Detection

We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pre-trained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.Comment: accepted for publication as a regular paper in the IEEE Transactions on Multimedi

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

End-to-end Learning for Short Text Expansion

Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is particularly challenging as a short text contains very sparse information, often too sparse for a machine learning algorithm to pick up useful signals. A common practice for analyzing short text is to first expand it with external information, which is usually harvested from a large collection of longer texts. In literature, short text expansion has been done with all kinds of heuristics. We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task. A novel deep memory network is proposed to automatically find relevant information from a collection of longer documents and reformulate the short text through a gating mechanism. Using short text classification as a demonstrating task, we show that the deep memory network significantly outperforms classical text expansion methods with comprehensive experiments on real world data sets.Comment: KDD'201

Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have only been applied to standard ad hoc retrieval tasks over web pages and newswire documents. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network) a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A pooling-based similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011--2014 show that our model significantly outperforms prior feature-based as well and existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models.Comment: AAAI 2019, 10 page

A review on the application of evolutionary computation to information retrieval

In this contribution, different proposals found in the specialized literature for the application of evolutionary computation to the field of information retrieval will be reviewed. To do so, different kinds of IR problems that have been solved by evolutionary algorithms are analyzed. Some of the specific existing approaches will be specifically described for some of these problems and the obtained results will be critically evaluated in order to give a clear view of the topic to the reader.CICYT under project TIC2002-03276University of Granada under project ‘‘Mejora de Metaheur ısticas mediante Hibridaci on y sus Aplicaciones

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