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Full-Text Articles in Physical Sciences and Mathematics
Sparsity Based Reflection Removal Using External Patch Search, Renjie Wan, Boxin Shi, Ah-Hwee Tan, Alex C. Kot
Sparsity Based Reflection Removal Using External Patch Search, Renjie Wan, Boxin Shi, Ah-Hwee Tan, Alex C. Kot
Research Collection School Of Computing and Information Systems
Reflection removal aims at separating the mixture of the desired background scenes and the undesired reflections, when the photos are taken through the glass. It has both aesthetic and practical applications which can largely improve the performance of many multimedia tasks. Existing reflection removal approaches heavily rely on scene priors such as separable sparse gradients brought by different levels of blur, and they easily fail when such priors are not observed in many real scenes. Sparse representation models and nonlocal image priors have shown their effectiveness in image restoration with self similarity. In this work, we propose a reflection removal …
Scalable Image Retrieval By Sparse Product Quantization, Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen
Scalable Image Retrieval By Sparse Product Quantization, Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen
Research Collection School Of Computing and Information Systems
Fast approximate nearest neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is product quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low-dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors, and thus, inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called sparse product quantization (SPQ) to encoding …
Online Cross-Modal Hashing For Web Image Retrieval, Liang Xie, Jialie Shen, Lei Zhu
Online Cross-Modal Hashing For Web Image Retrieval, Liang Xie, Jialie Shen, Lei Zhu
Research Collection School Of Computing and Information Systems
Cross-modal hashing (CMH) is an efficient technique for the fast retrieval of web image data, and it has gained a lot of attentions recently. However, traditional CMH methods usually apply batch learning for generating hash functions and codes. They are inefficient for the retrieval of web images which usually have streaming fashion. Online learning can be exploited for CMH. But existing online hashing methods still cannot solve two essential problems: Efficient updating of hash codes and analysis of cross-modal correlation. In this paper, we propose Online Cross-modal Hashing (OCMH) which can effectively address the above two problems by learning the …
Soml: Sparse Online Metric Learning With Application To Image Retrieval, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Ji Wan, Jintao Li
Soml: Sparse Online Metric Learning With Application To Image Retrieval, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Ji Wan, Jintao Li
Research Collection School Of Computing and Information Systems
Image similarity search plays a key role in many multimedia applications, where multimedia data (such as images and videos) are usually represented in high-dimensional feature space. In this paper, we propose a novel Sparse Online Metric Learning (SOML) scheme for learning sparse distance functions from large-scale high-dimensional data and explore its application to image retrieval. In contrast to many existing distance metric learning algorithms that are often designed for low-dimensional data, the proposed algorithms are able to learn sparse distance metrics from high-dimensional data in an efficient and scalable manner. Our experimental results show that the proposed method achieves better …
Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao
Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao
Research Collection School Of Computing and Information Systems
Recent years have witnessed extensive studies on distance metric learning (DML) for improving similarity search in multimedia information retrieval tasks. Despite their successes, most existing DML methods suffer from two critical limitations: (i) they typically attempt to learn a linear distance function on the input feature space, in which the assumption of linearity limits their capacity of measuring the similarity on complex patterns in real-world applications; (ii) they are often designed for learning distance metrics on uni-modal data, which may not effectively handle the similarity measures for multimedia objects with multimodal representations. To address these limitations, in this paper, we …
A Unified Learning Framework For Auto Face Annotation By Mining Web Facial Images, Dayong Wang, Steven C. H. Hoi, Ying He
A Unified Learning Framework For Auto Face Annotation By Mining Web Facial Images, Dayong Wang, Steven C. H. Hoi, Ying He
Research Collection School Of Computing and Information Systems
Auto face annotation plays an important role in many real-world multimedia information and knowledge management systems. Recently there is a surge of research interests in mining weakly-labeled facial images on the internet to tackle this long-standing research challenge in computer vision and image understanding. In this paper, we present a novel unified learning framework for face annotation by mining weakly labeled web facial images through interdisciplinary efforts of combining sparse feature representation, content-based image retrieval, transductive learning and inductive learning techniques. In particular, we first introduce a new search-based face annotation paradigm using transductive learning, and then propose an effective …
Boosting Multi-Kernel Locality-Sensitive Hashing For Scalable Image Retrieval, Hao Xia, Steven C. H. Hoi, Pengcheng Wu, Rong Jin
Boosting Multi-Kernel Locality-Sensitive Hashing For Scalable Image Retrieval, Hao Xia, Steven C. H. Hoi, Pengcheng Wu, Rong Jin
Research Collection School Of Computing and Information Systems
Similarity search is a key challenge for multimedia retrieval applications where data are usually represented in high-dimensional space. Among various algorithms proposed for similarity search in high-dimensional space, Locality-Sensitive Hashing (LSH) is the most popular one, which recently has been extended to Kernelized Locality-Sensitive Hashing (KLSH) by exploiting kernel similarity for better retrieval efficacy. Typically, KLSH works only with a single kernel, which is often limited in real-world multimedia applications, where data may originate from multiple resources or can be represented in several different forms. For example, in content-based multimedia retrieval, a variety of features can be extracted to represent …
Semantics-Preserving Bag-Of-Words Models And Applications, Lei Wu, Steven C. H. Hoi, Nenghai Yu
Semantics-Preserving Bag-Of-Words Models And Applications, Lei Wu, Steven C. H. Hoi, Nenghai Yu
Research Collection School Of Computing and Information Systems
The Bag-of-Words (BoW) model is a promising image representation technique for image categorization and annotation tasks. One critical limitation of existing BoW models is that much semantic information is lost during the codebook generation process, an important step of BoW. This is because the codebook generated by BoW is often obtained via building the codebook simply by clustering visual features in Euclidian space. However, visual features related to the same semantics may not distribute in clusters in the Euclidian space, which is primarily due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a …
A Boosting Framework For Visuality-Preserving Distance Metric Learning And Its Application To Medical Image Retrieval, Yang Liu, Rong Jin, Lily Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Steven C. H. Hoi, Mahadev Satyanarayanan
A Boosting Framework For Visuality-Preserving Distance Metric Learning And Its Application To Medical Image Retrieval, Yang Liu, Rong Jin, Lily Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Steven C. H. Hoi, Mahadev Satyanarayanan
Research Collection School Of Computing and Information Systems
Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one …
Batch Mode Active Learning With Applications To Text Categorization And Image Retrieval, Steven C. H. Hoi, Rong Jin, Michael R. Lyu
Batch Mode Active Learning With Applications To Text Categorization And Image Retrieval, Steven C. H. Hoi, Rong Jin, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Most machine learning tasks in data classification and information retrieval require manually labeled data examples in the training stage. The goal of active learning is to select the most informative examples for manual labeling in these learning tasks. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient, since the classification model has to be retrained for every acquired labeled example. It is also inappropriate for the setup of information retrieval tasks where the user's relevance feedback is often provided for the top K retrieved items. In …
Stochastic Modeling Western Paintings For Effective Classification, Jialie Shen
Stochastic Modeling Western Paintings For Effective Classification, Jialie Shen
Research Collection School Of Computing and Information Systems
As one of the most important cultural heritages, classical western paintings have always played a special role in human live and been applied for many different purposes. While image classification is the subject of a plethora of related publications, relatively little attention has been paid to automatic categorization of western classical paintings which could be a key technique of modern digital library, museums and art galleries. This paper studies automatic classification on large western painting image collection. We propose a novel framework to support automatic classification on large western painting image collections. With this framework, multiple visual features can be …
Semi-Supervised Svm Batch Mode Active Learning For Image Retrieval, Steven Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Semi-Supervised Svm Batch Mode Active Learning For Image Retrieval, Steven Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to …
Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval, Steven Hoi, Wei Liu, Shih-Fu Chang
Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval, Steven Hoi, Wei Liu, Shih-Fu Chang
Research Collection School Of Computing and Information Systems
Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called “Collaborative Image Retrieval” (CIR). To effectively explore the log data, we propose a novel semi-supervised distance metric learning technique, called “Laplacian Regularized Metric Learning” (LRML), for learning robust distance metrics for CIR. Different from previous methods, the proposed LRML method integrates both …
Near-Duplicate Keyframe Retrieval With Visual Keywords And Semantic Context, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Near-Duplicate Keyframe Retrieval With Visual Keywords And Semantic Context, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Near-duplicate keyframes (NDK) play a unique role in large-scale video search, news topic detection and tracking. In this paper, we propose a novel NDK retrieval approach by exploring both visual and textual cues from the visual vocabulary and semantic context respectively. The vocabulary, which provides entries for visual keywords, is formed by the clustering of local keypoints. The semantic context is inferred from the speech transcript surrounding a keyframe. We experiment the usefulness of visual keywords and semantic context, separately and jointly, using cosine similarity and language models. By linearly fusing both modalities, performance improvement is reported compared with the …
Learning Distance Metrics With Contextual Constraints For Image Retrieval, Steven C. H. Hoi, Wei Liu, Michael R. Lyu, Wei-Ying Ma
Learning Distance Metrics With Contextual Constraints For Image Retrieval, Steven C. H. Hoi, Wei Liu, Michael R. Lyu, Wei-Ying Ma
Research Collection School Of Computing and Information Systems
Relevant Component Analysis (RCA) has been proposed for learning distance metrics with contextual constraints for image retrieval. However, RCA has two important disadvantages. One is the lack of exploiting negative constraints which can also be informative, and the other is its incapability of capturing complex nonlinear relationships between data instances with the contextual information. In this paper, we propose two algorithms to overcome these two disadvantages, i.e., Discriminative Component Analysis (DCA) and Kernel DCA. Compared with other complicated methods for distance metric learning, our algorithms are rather simple to understand and very easy to solve. We evaluate the performance of …
A Semi-Supervised Active Learning Framework For Image Retrieval, Steven Hoi, Michael R. Lyu
A Semi-Supervised Active Learning Framework For Image Retrieval, Steven Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Although recent studies have shown that unlabeled data are beneficial to boosting the image retrieval performance, very few approaches for image retrieval can learn with labeled and unlabeled data effectively. This paper proposes a novel semi-supervised active learning framework comprising a fusion of semi-supervised learning and support vector machines. We provide theoretical analysis of the active learning framework and present a simple yet effective active learning algorithm for image retrieval. Experiments are conducted on real-world color images to compare with traditional methods. The promising experimental results show that our proposed scheme significantly outperforms the previous approaches.
Integrating User Feedback Log Into Relevance Feedback By Coupled Svm For Content-Based Image Retrieval, Steven C. H. Hoi, Michael R. Lyu, Rong Jin
Integrating User Feedback Log Into Relevance Feedback By Coupled Svm For Content-Based Image Retrieval, Steven C. H. Hoi, Michael R. Lyu, Rong Jin
Research Collection School Of Computing and Information Systems
Relevance feedback has been shown as an important tool to boost the retrieval performance in content-based image retrieval. In the past decade, various algorithms have been proposed to formulate relevance feedback in contentbased image retrieval. Traditional relevance feedback techniques mainly carry out the learning tasks by focusing lowlevel visual features of image content with little consideration on log information of user feedback. However, from a long-term learning perspective, the user feedback log is one of the most important resources to bridge the semantic gap problem in image retrieval. In this paper we propose a novel technique to integrate the log …
Biased Support Vector Machine For Relevance Feedback In Image Retrieval, Steven Hoi, Chi-Hang Chan, Kaizhu Huang, Michael R. Lyu, Irwin King
Biased Support Vector Machine For Relevance Feedback In Image Retrieval, Steven Hoi, Chi-Hang Chan, Kaizhu Huang, Michael R. Lyu, Irwin King
Research Collection School Of Computing and Information Systems
Recently, support vector machines (SVMs) have been engaged on relevance feedback tasks in content-based image retrieval. Typical approaches by SVMs treat the relevance feedback as a strict binary classification problem. However, these approaches do not consider an important issue of relevance feedback, i.e. the unbalanced dataset problem, in which the negative instances largely outnumber the positive instances. For solving this problem, we propose a novel technique to formulate the relevance feedback based on a modified SVM called biased support vector machine (Biased SVM or BSVM). Mathematical formulation and explanations are provided for showing the advantages. Experiments are conducted to evaluate …