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Displaying 1-50 out of 76 total
Trusted Data Sharing over Untrusted Cloud Storage Providers
Found in: Cloud Computing Technology and Science, IEEE International Conference on
By Gansen Zhao, Chunming Rong, Jin Li, Feng Zhang, Yong Tang
Issue Date:December 2010
pp. 97-103
Cloud computing has been acknowledged as one of the prevaling models for providing IT capacities. The off-premises computing paradigm that comes with cloud computing has incurred great concerns on the security of data, especially the integrity and confiden...
 
Web Image Retrieval Re-Ranking with Relevance Model
Found in: Web Intelligence, IEEE / WIC / ACM International Conference on
By Wei-Hao Lin, Rong Jin, Alexander Hauptmann
Issue Date:October 2003
pp. 242
Web image retrieval is a challenging task that requires efforts from image processing, link structure analysis, and web text retrieval. Since content-based image retrieval is still considered very difficult, most current large-scale web image search engine...
 
Design and Implementation of an Open Programmable Router Compliant to IETF ForCES Specifications
Found in: International Conference on Networking
By Weiming Wang, Ligang Dong, Bin Zhuge, Ming Gao, Fenggen Jia, Rong Jin, Jin Yu, Xiaochun Wu
Issue Date:April 2007
pp. 82
IETF ForCES (Forwarding and Control Element Separation) is defining specifications for interfaces and modular resources abstractions of network equipments. ForCES brings flexible, programmable, and cost-effective advantages over tradi-tional architectures ...
 
Online Feature Selection and Its Applications
Found in: IEEE Transactions on Knowledge and Data Engineering
By Jialei Wang,Peilin Zhao,Steven C.H. Hoi,Rong Jin
Issue Date:March 2014
pp. 698-710
Feature selection is an important technique for data mining. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient a...
 
Large-Scale Image Annotation by Efficient and Robust Kernel Metric Learning
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Zheyun Feng,Rong Jin,Anil Jain
Issue Date:December 2013
pp. 1609-1616
One of the key challenges in search-based image annotation models is to define an appropriate similarity measure between images. Many kernel distance metric learning (KML) algorithms have been developed in order to capture the nonlinear relationships betwe...
 
Tag Completion for Image Retrieval
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Lei Wu, Rong Jin,A. K. Jain
Issue Date:March 2013
pp. 716-727
Many social image search engines are based on keyword/tag matching. This is because tag-based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR is highly dependent on the availability and quality of manual tags. Recen...
 
Robust Ensemble Clustering by Matrix Completion
Found in: 2012 IEEE 12th International Conference on Data Mining (ICDM)
By Jinfeng Yi,Tianbao Yang,Rong Jin,Anil K. Jain,Mehrdad Mahdavi
Issue Date:December 2012
pp. 1176-1181
Data clustering is an important task and has found applications in numerous real-world problems. Since no single clustering algorithm is able to identify all different types of cluster shapes and structures, ensemble clustering was proposed to combine diff...
 
Efficient Kernel Clustering Using Random Fourier Features
Found in: 2012 IEEE 12th International Conference on Data Mining (ICDM)
By Radha Chitta,Rong Jin,Anil K. Jain
Issue Date:December 2012
pp. 161-170
Kernel clustering algorithms have the ability to capture the non-linear structure inherent in many real world data sets and thereby, achieve better clustering performance than Euclidean distance based clustering algorithms. However, their quadratic computa...
 
Tattoo Image Matching and Retrieval
Found in: Computer
By Anil K. Jain,Rong Jin,Jung-Eun Lee
Issue Date:May 2012
pp. 93-96
An automated tattoo image matching system achieves significantly better results for forensic and law enforcement applications than traditional keyword-based matching.
 
Image Retrieval in Forensics: Tattoo Image Database Application
Found in: IEEE Multimedia
By Jung-Eun Lee,Rong Jin,Anil K. Jain,Wei Tong
Issue Date:January 2012
pp. 40-49
Two modifications to the Tattoo-ID system, based on automatic content-based image retrieval, help improve its retrieval accuracy, particularly for low-quality tattoo image queries.
 
Multi-label learning with incomplete class assignments
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By S. S. Bucak, Rong Jin,A. K. Jain
Issue Date:June 2011
pp. 2801-2808
We consider a special type of multi-label learning where class assignments of training examples are incomplete. As an example, an instance whose true class assignment is (c_1, c_2, c_3) is only assigned to class c_1 when it is used as a training sample. We...
 
A probabilistic representation for efficient large scale visual recognition tasks
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By S. Bhattacharya,R. Sukthankar, Rong Jin,M. Shah
Issue Date:June 2011
pp. 2593-2600
In this paper, we present an efficient alternative to the traditional vocabulary based on bag-of-visual words (BoW) used for visual classification tasks. Our representation is both conceptually and computationally superior to the bag-of-visual words: (1) W...
 
Unsupervised Ensemble Ranking: Application to Large-Scale Image Retrieval
Found in: Pattern Recognition, International Conference on
By Jung-Eun Lee, Rong Jin, Anil K. Jain
Issue Date:August 2010
pp. 3902-3906
The continued explosion in the growth of image and video databases makes automatic image search and retrieval an extremely important problem. Among the various approaches to Content-based Image Retrieval (CBIR), image similarity based on local point descri...
 
Online visual vocabulary pruning using pairwise constraints
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Pavan K. Mallapragada, Rong Jin, Anil K. Jain
Issue Date:June 2010
pp. 3073-3080
Given a pair of images represented using bag-of-visual-words and a label corresponding to whether the images are “related”(must-link constraint) or “unrelated” (cannot-link constraint), we address the problem of selecting a subset of visual words that are ...
 
The Research on Teaching Method of Basics Course of Computer based on Cluster Analysis
Found in: Computer and Information Technology, International Conference on
By ZhiXin Tie, Rong Jin, Hong Zhuang, ZhaoQing Wang
Issue Date:July 2010
pp. 2001-2004
Due to the difference between rural and urban and regional disparity of computer basic education produced at their middle school period, computer application level of the freshmen make a great difference. According to this situation, corresponding skill su...
 
Learning a distance metric from multi-instance multi-label data
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Rong Jin, Shijun Wang, Zhi-Hua Zhou
Issue Date:June 2009
pp. 896-902
Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. An example application of MIML is visual object recognition in which each image...
 
Efficient Algorithm for Localized Support Vector Machine
Found in: IEEE Transactions on Knowledge and Data Engineering
By Haibin Cheng, Pang-Ning Tan, Rong Jin
Issue Date:April 2010
pp. 537-549
This paper presents a framework called Localized Support Vector Machine (LSVM) for classifying data with nonlinear decision surfaces. Instead of building a sophisticated global model from the training data, LSVM constructs multiple linear SVMs, each of whi...
 
Inferring functional cortical networks from spike train ensembles using Dynamic Bayesian Networks
Found in: Acoustics, Speech, and Signal Processing, IEEE International Conference on
By Seif Eldawlatly, Rong Jin, Karim Oweiss
Issue Date:April 2009
pp. 3489-3492
A fundamental goal in systems neuroscience is to infer the functional connectivity among neuronal elements coordinating information processing in the brain. In this work, we investigate the applicability of Dynamic Bayesian Networks (DBN) in inferring the ...
 
Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval
Found in: IEEE Transactions on Knowledge and Data Engineering
By Steven C.H. Hoi, Rong Jin, Michael R. Lyu
Issue Date:September 2009
pp. 1233-1248
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. M...
 
A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Liu Yang, Rong Jin, Lily Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Steven C.H. Hoi, Mahadev Satyanarayanan
Issue Date:January 2010
pp. 30-44
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 ex...
 
SemiBoost: Boosting for Semi-Supervised Learning
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Pavan Kumar Mallapragada, Rong Jin, Anil K. Jain, Yi Liu
Issue Date:November 2009
pp. 2000-2014
Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled d...
 
Rank-based distance metric learning: An application to image retrieval
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Jung-Eun Lee, Rong Jin, Anil K. Jain
Issue Date:June 2008
pp. 1-8
We present a novel approach to learn distance metric for information retrieval. Learning distance metric from a number of queries with side information, i.e., relevance judgements, has been studied widely, for example pairwise constraint-based distance met...
 
Unifying discriminative visual codebook generation with classifier training for object category recognition
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Liu Yang, Rong Jin, Rahul Sukthankar, Frederic Jurie
Issue Date:June 2008
pp. 1-8
The idea of representing images using a bag of visual words is currently popular in object category recognition. Since this representation is typically constructed using unsupervised clustering, the resulting visual words may not capture the desired inform...
 
Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Liu Yang, Rong Jin, Caroline Pantofaru, Rahul Sukthankar
Issue Date:June 2007
pp. 1-8
A popular approach to problems in image classification is to represent the image as a bag of visual words and then employ a classifier to categorize the image. Unfortunately, a significant shortcoming of this approach is that the clustering and classificat...
 
Correlated Label Propagation with Application to Multi-label Learning
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Feng Kang, Rong Jin, Rahul Sukthankar
Issue Date:June 2006
pp. 1719-1726
Many computer vision applications, such as scene analysis and medical image interpretation, are ill-suited for traditional classification where each image can only be associated with a single class. This has stimulated recent work in multi-label learning w...
 
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
Found in: IEEE Transactions on Knowledge and Data Engineering
By Steven C.H. Hoi, Michael R. Lyu, Rong Jin
Issue Date:April 2006
pp. 509-524
Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback...
 
Integrating User Feedback Log into Relevance Feedback by Coupled SVM for Content-Based Image Retrieval
Found in: Data Engineering Workshops, 22nd International Conference on
By Steven C. H. Hoi, Michael R. Lyu, Rong Jin
Issue Date:April 2005
pp. 1177
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. Tradition...
 
Learning to identify video shots with people based on face detection
Found in: Multimedia and Expo, IEEE International Conference on
By Rong Jin, A.G. Hauptmann
Issue Date:July 2003
pp. 293-296
We examine how to identify video shots with at least two humans using only detected face information. While face detection is much more reliable than shape based people classification in broadcast video, one particular difficulty is that, when there are se...
 
A Probabilistic Model for Camera Zoom Detection
Found in: Pattern Recognition, International Conference on
By Rong Jin, Yanjun Qi, Alexander Hauptmann
Issue Date:August 2002
pp. 30859
Camera motion detection is essential for automated video analysis. We propose a new probabilistic model for detecting zoom-in/zoom-out operations. The model uses EM to estimate the probability of a zoom versus a non-zoom operation from standard MPEG motion...
 
Multiple Kernel Learning for Visual Object Recognition: A Review
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Serhat S. Bucak,Rong Jin,Anil K. Jain
Issue Date:July 2014
pp. 1-1
Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels for a given recognition task. A number of studies have shown that MKL is a useful tool for object recognition, where each image is represented by multiple sets of f...
 
Online Multiple Kernel Similarity Learning for Visual Search
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Hao Xia,Steven C. H. Hoi, Rong Jin, Peilin Zhao
Issue Date:March 2014
pp. 536-549
Recent years have witnessed a number of studies on distance metric learning to improve visual similarity search in content-based image retrieval (CBIR). Despite their successes, most existing methods on distance metric learning are limited in two aspects. ...
 
Active Learning by Querying Informative and Representative Examples
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Sheng-Jun Huang,Rong Jin,Zhi-Hua Zhou
Issue Date:February 2014
pp. 1
Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select...
 
Compressed Hashing
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Yue Lin,Rong Jin,Deng Cai,Shuicheng Yan,Xuelong Li
Issue Date:June 2013
pp. 446-451
Recent studies have shown that hashing methods are effective for high dimensional nearest neighbor search. A common problem shared by many existing hashing methods is that in order to achieve a satisfied performance, a large number of hash tables (i.e., lo...
 
Learning Bregman Distance Functions for Semi-Supervised Clustering
Found in: IEEE Transactions on Knowledge and Data Engineering
By Lei Wu, Steven C.H. Hoi, Rong Jin, Jianke Zhu, Nenghai Yu
Publication Date: October 2010
pp. N/A
Learning distance functions with side information plays a key role in many data mining applications. Conventional distance metric learning approaches often assume the target distance function is represented in some form of Mahalanobis distance. These appro...
 
Online feature selection for mining big data
Found in: Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine '12)
By Jialei Wang, Peilin Zhao, Rong Jin, Steven C. H. Hoi
Issue Date:August 2012
pp. 93-100
Most studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or the access to it is expens...
     
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
Found in: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (SIGIR '12)
By Hao Xia, Pengcheng Wu, Rong Jin, Steven C.H. Hoi
Issue Date:August 2012
pp. 55-64
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 ...
     
Introduction to the Special Section on Distance Metric Learning in Intelligent Systems
Found in: ACM Transactions on Intelligent Systems and Technology (TIST)
By Jinhui Tang, Rong Jin, Steven C. H. Hoi, Zhi-Hua Zhou
Issue Date:May 2012
pp. 1-2
Current and proposed remote space missions, such as the proposed aerial exploration of Titan by an aerobot, often can collect more data than can be communicated back to Earth. Autonomous selective downlink algorithms can choose informative subsets of data ...
     
Gang and moniker identification by graffiti matching
Found in: Proceedings of the 3rd international ACM workshop on Multimedia in forensics and intelligence (MiFor '11)
By Anil K. Jain, Jung-Eun Lee, Rong Jin, Wei Tong
Issue Date:November 2011
pp. 1-6
Identifying criminal gangs and monikers is one of the most important tasks for graffiti analysis in low enforcement. In current practice, this is typically performed manually by the law enforcement officers, which is not only time-consuming but also result...
     
Machine learning for information retrieval
Found in: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information (SIGIR '11)
By Luo Si, Rong Jin
Issue Date:July 2011
pp. 1293-1294
In recent years, we have witnessed successful application of machine learning techniques to a wide range of information retrieval problems, including Web search engines, recommendation systems, online advertising, etc. It is thus critical for researchers i...
     
Unsupervised transfer classification: application to text categorization
Found in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10)
By Anil K. Jain, Rong Jin, Tianbao Yang, Wei Tong, Yang Zhou
Issue Date:July 2010
pp. 1159-1168
We study the problem of building the classification model for a target class in the absence of any labeled training example for that class. To address this difficult learning problem, we extend the idea of transfer learning by assuming that the following s...
     
Exploitation and exploration in a performance based contextual advertising system
Found in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10)
By Jianchang Mao, Rong Jin, Ruofei Zhang, Wei Li, Xuerui Wang, Ying Cui
Issue Date:July 2010
pp. 27-36
The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of online data inevitably makes an advertising system choose between maximizing it...
     
First ACM SIGMM international workshop onsocial media (WSM'09)
Found in: Proceedings of the seventeen ACM international conference on Multimedia (MM '09)
By Dong Xu, Irwin King, Jiebo Luo, Rong Jin, Steven C.H. Hoi, Susanne Boll
Issue Date:October 2009
pp. 1161-1162
The ACM SIGMM International Workshop on Social Media (WSM'09) is the first workshop held in conjunction with the ACM International Multimedia Conference (MM'09) at Bejing, P.R. China, 2009. This workshop provides a forum for researchers and practitioners f...
     
Distance metric learning from uncertain side information with application to automated photo tagging
Found in: Proceedings of the seventeen ACM international conference on Multimedia (MM '09)
By Jianke Zhu, Lei Wu, Nenghai Yu, Rong Jin, Steven C.H. Hoi
Issue Date:October 2009
pp. 135-144
Automated photo tagging is essential to make massive unlabeled photos searchable by text search engines. Conventional image annotation approaches, though working reasonably well on small testbeds, are either computationally expensive or inaccurate when dea...
     
Graffiti-ID: matching and retrieval of graffiti images
Found in: Proceedings of the First ACM workshop on Multimedia in forensics (MiFor '09)
By Anil K. Jain, Jung-Eun Lee, Rong Jin
Issue Date:October 2009
pp. 1-6
Graffiti are abundant in most urban neighborhoods and are considered a nuisance and an eyesore. Yet, law enforcement agencies have found them to be useful for understanding gang activities, and uncovering the extent of a gang's territory in large metropoli...
     
An efficient key point quantization algorithm for large scale image retrieval
Found in: Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining (LS-MMRM '09)
By Anil K. Jain, Fengjie Li, Jung-Eun Lee, Rong Jin, Wei Tong
Issue Date:October 2009
pp. 89-96
We focus on the problem of large-scale near duplicate image retrieval. Recent studies have shown that local image features, often referred to as key points, are effective for near duplicate image retrieval. The most popular approach for key point based ima...
     
Combining link and content for community detection: a discriminative approach
Found in: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09)
By Rong Jin, Shenghuo Zhu, Tianbao Yang, Yun Chi
Issue Date:June 2009
pp. 1-24
In this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web. Most existing approaches combine link and content information by a generative mode...
     
Online learning by ellipsoid method
Found in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09)
By Jieping Ye, Liu Yang, Rong Jin
Issue Date:June 2009
pp. 1-8
In this work, we extend the ellipsoid method, which was originally designed for convex optimization, for online learning. The key idea is to approximate by an ellipsoid the classification hypotheses that are consistent with all the training examples receiv...
     
Non-monotonic feature selection
Found in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09)
By Irwin King, Jieping Ye, Michael R. Lyu, Rong Jin, Zenglin Xu
Issue Date:June 2009
pp. 1-8
We consider the problem of selecting a subset of m most informative features where m is the number of required features. This feature selection problem is essentially a combinatorial optimization problem, and is usually solved by an approximation. Conventi...
     
Semisupervised SVM batch mode active learning with applications to image retrieval
Found in: ACM Transactions on Information Systems (TOIS)
By Jianke Zhu, Michael R. Lyu, Rong Jin, Steven C. H. Hoi
Issue Date:May 2009
pp. 1-29
Support vector machine (SVM) active learning is one popular and successful technique for relevance feedback in content-based image retrieval (CBIR). Despite the success, conventional SVM active learning has two main drawbacks. First, the performance of SVM...
     
Semi-supervised text categorization by active search
Found in: Proceeding of the 17th ACM conference on Information and knowledge mining (CIKM '08)
By Irwin King, Kaizhu Huang, Michael R. Lyu, Rong Jin, Zenglin Xu
Issue Date:October 2008
pp. 1001-1001
In automated text categorization, given a small number of labeled documents, it is very challenging, if not impossible, to build a reliable classifier that is able to achieve high classification accuracy. To address this problem, a novel web-assisted text ...
     
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