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Displaying 1-50 out of 166 total
Cross Domain Random Walk for Query Intent Pattern Mining from Search Engine Log
Found in: Data Mining, IEEE International Conference on
By Siyu Gu,Jun Yan,Lei Ji,Shuicheng Yan,Junshi Huang,Ning Liu,Ying Chen,Zheng Chen
Issue Date:December 2011
pp. 221-230
Understanding search intents of users through their condensed short queries has attracted much attention both in academia and industry. The search intents of users are generally assumed to be associated with various query patterns, such as
 
A Novel Contrast Co-learning Framework for Generating High Quality Training Data
Found in: Data Mining, IEEE International Conference on
By Zeyu Zheng, Jun Yan, Shuicheng Yan, Ning Liu, Zheng Chen, Ming Zhang
Issue Date:December 2010
pp. 649-658
The good performances of most classical learning algorithms are generally founded on high quality training data, which are clean and unbiased. The availability of such data is however becoming much harder than ever in many real world problems due to the di...
 
Trace-Oriented Feature Analysis for Large-Scale Text Data Dimension Reduction
Found in: IEEE Transactions on Knowledge and Data Engineering
By Jun Yan, Ning Liu, Shuicheng Yan, Qiang Yang, Weiguo (Patrick) Fan, Wei Wei, Zheng Chen
Issue Date:July 2011
pp. 1103-1117
Dimension reduction for large-scale text data is attracting much attention nowadays due to the rapid growth of the World Wide Web. We can categorize those popular dimension reduction algorithms into two groups: feature extraction and feature selection algo...
 
Synthesizing Novel Dimension Reduction Algorithms in Matrix Trace Oriented Optimization Framework
Found in: Data Mining, IEEE International Conference on
By Jun Yan, Ning Liu, Shuicheng Yan, Qiang Yang, Zheng Chen
Issue Date:December 2009
pp. 598-606
Dimension Reduction (DR) algorithms are generally categorized into feature extraction and feature selection algorithms. In the past, few works have been done to contrast and unify the two algorithm categories. In this work, we introduce a matrix trace orie...
 
Unified Solution to Nonnegative Data Factorization Problems
Found in: Data Mining, IEEE International Conference on
By Xiaobai Liu, Shuicheng Yan, Jun Yan, Hai Jin
Issue Date:December 2009
pp. 307-316
In this paper, we restudy the non-convex data factorization problems (regularized or not, unsupervised or supervised), where the optimization is confined in the \emph{nonnegative} orthant, and provide a \emph{unified} convergency provable solution based on...
 
Learning the Latent Semantic Space for Ranking in Text Retrieval
Found in: Data Mining, IEEE International Conference on
By Jun Yan, Shuicheng Yan, Ning Liu, Zheng Chen
Issue Date:December 2008
pp. 1115-1120
Subspace learning techniques for text analysis, such as Latent Semantic Indexing (LSI), have been widely studied in the past decade. However, to our best knowledge, no previous study has leveraged the rank information for subspace learning in ranking tasks...
 
Web Query Prediction by Unifying Model
Found in: Data Mining Workshops, International Conference on
By Ning Liu, Jun Yan, Shuicheng Yan, Weiguo Fan, Zheng Chen
Issue Date:December 2008
pp. 436-441
Recently, many commercial products, such as Google Trends and Yahoo! Buzz, are released to monitor the past search engine query frequency trend. However, little research has been devoted for predicting the upcoming query trend, which is of great importance...
 
Local Word Bag Model for Text Categorization
Found in: Data Mining, IEEE International Conference on
By Wen Pu, Ning Liu, Shuicheng Yan, Jun Yan, Kunqing Xie, Zheng Chen
Issue Date:October 2007
pp. 625-630
Many text processing applications adopted the Bag of Words (BOW) model representation of documents, in which each document is represented as a vector of weighted terms or n-grams, and then cosine distance between two vectors is used as the similarity measu...
 
A Novel Scalable Algorithm for Supervised Subspace Learning
Found in: Data Mining, IEEE International Conference on
By Jun Yan, Ning Liu, Benyu Zhang, Qiang Yang, Shuicheng Yan, Zheng Chen
Issue Date:December 2006
pp. 721-730
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as Principal Component Analysis (PCA) do not make use of the class information, and Linear Discriminant Analysis (LD...
 
Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Preprocessing
Found in: IEEE Transactions on Knowledge and Data Engineering
By Jun Yan, Benyu Zhang, Ning Liu, Shuicheng Yan, Qiansheng Cheng, Weiguo Fan, Qiang Yang, Wensi Xi, Zheng Chen
Issue Date:March 2006
pp. 320-333
Dimensionality reduction is an essential data preprocessing technique for large-scale and streaming data classification tasks. It can be used to improve both the efficiency and the effectiveness of classifiers. Traditional dimensionality reduction approach...
 
Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization
Found in: Data Mining, IEEE International Conference on
By Chenyong Hu, Benyu Zhang, Shuicheng Yan, Qiang Yang, Jun Yan, Zheng Chen, Wei-Ying Ma
Issue Date:November 2004
pp. 407-410
Association rules are traditionally designed to capture statistical relationship among itemsets in a given database. To additionally capture the quantitative association knowledge, F.Korn et al recently proposed a paradigm named Ratio Rules for quantifiabl...
 
Bin Ratio-Based Histogram Distances and Their Application to Image Classification
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Weiming Hu,Nianhua Xie,Ruiguang Hu,Haibin Ling,Qiang Chen,Shuicheng Yan,Stephen Maybank
Issue Date:December 2014
pp. 1-1
Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large c...
 
Fashion Analysis: Current Techniques and Future Directions
Found in: IEEE MultiMedia
By Si Liu,Luoqi Liu,Shuicheng Yan
Issue Date:April 2014
pp. 72-79
Driven by the huge profit potential in the fashion industry, intelligent fashion analysis based on techniques for clothing and makeover analysis is receiving much attention in the multimedia and computer vision literature. This article surveys the state-of...
 
Correntropy Induced L2 Graph for Robust Subspace Clustering
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Canyi Lu,Jinhui Tang,Min Lin,Liang Lin,Shuicheng Yan,Zhouchen Lin
Issue Date:December 2013
pp. 1801-1808
In this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy data points into their underlying subspaces. A large pool of previous subspace clustering methods focus on the graph construction by different re...
 
Correlation Adaptive Subspace Segmentation by Trace Lasso
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Canyi Lu,Jiashi Feng,Zhouchen Lin,Shuicheng Yan
Issue Date:December 2013
pp. 1345-1352
This paper studies the subspace segmentation problem. Given a set of data points drawn from a union of subspaces, the goal is to partition them into their underlying subspaces they were drawn from. The spectral clustering method is used as the framework. I...
 
Semantic Segmentation without Annotating Segments
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Wei Xia,Csaba Domokos,Jian Dong,Loong-Fah Cheong,Shuicheng Yan
Issue Date:December 2013
pp. 2176-2183
Numerous existing object segmentation frameworks commonly utilize the object bounding box as a prior. In this paper, we address semantic segmentation assuming that object bounding boxes are provided by object detectors, but no training data with annotated ...
 
Hierarchical Part Matching for Fine-Grained Visual Categorization
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Lingxi Xie,Qi Tian,Richang Hong,Shuicheng Yan,Bo Zhang
Issue Date:December 2013
pp. 1641-1648
As a special topic in computer vision, fine-grained visual categorization (FGVC) has been attracting growing attention these years. Different with traditional image classification tasks in which objects have large inter-class variation, the visual concepts...
 
How Related Exemplars Help Complex Event Detection in Web Videos?
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Yi Yang,Zhigang Ma,Zhongwen Xu,Shuicheng Yan,Alexander G. Hauptmann
Issue Date:December 2013
pp. 2104-2111
Compared to visual concepts such as actions, scenes and objects, complex event is a higher level abstraction of longer video sequences. For example, a "marriage proposal" event is described by multiple objects (e.g., ring, faces), scenes (e.g., i...
 
Robust Object Tracking with Online Multi-lifespan Dictionary Learning
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Junliang Xing,Jin Gao,Bing Li,Weiming Hu,Shuicheng Yan
Issue Date:December 2013
pp. 665-672
Recently, sparse representation has been introduced for robust object tracking. By representing the object sparsely, i.e., using only a few templates via L1-norm minimization, these so-called L1-trackers exhibit promising tracking results. In this work, we...
 
A Deformable Mixture Parsing Model with Parselets
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Jian Dong,Qiang Chen,Wei Xia,Zhongyang Huang,Shuicheng Yan
Issue Date:December 2013
pp. 3408-3415
In this work, we address the problem of human parsing, namely partitioning the human body into semantic regions, by using the novel Parse let representation. Previous works often consider solving the problem of human pose estimation as the prerequisite of ...
 
Forward Basis Selection for Pursuing Sparse Representations over a Dictionary
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Xiao-Tong Yuan, Shuicheng Yan
Issue Date:December 2013
pp. 3025-3036
The forward greedy selection algorithm of Frank and Wolfe has recently been applied with success to coordinate-wise sparse learning problems, characterized by a tradeoff between sparsity and accuracy. In this paper, we generalize this method to the setup o...
 
Min-Max Hash for Jaccard Similarity
Found in: 2013 IEEE International Conference on Data Mining (ICDM)
By Jianqiu Ji,Jianmin Li,Shuicheng Yan,Qi Tian,Bo Zhang
Issue Date:December 2013
pp. 301-309
Min-wise hash is a widely-used hashing method for scalable similarity search in terms of Jaccard similarity, while in practice it is necessary to compute many such hash functions for certain precision, leading to expensive computational cost. In this paper...
 
Fast Detection of Dense Subgraphs with Iterative Shrinking and Expansion
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Hairong Liu,L. J. Latecki, Shuicheng Yan
Issue Date:September 2013
pp. 2131-2142
In this paper, we propose an efficient algorithm to detect dense subgraphs of a weighted graph. The proposed algorithm, called the shrinking and expansion algorithm (SEA), iterates between two phases, namely, the expansion phase and the shrink phase, until...
 
Robust Recovery of Subspace Structures by Low-Rank Representation
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, Yi Ma
Issue Date:January 2013
pp. 171-184
In this paper, we address the subspace clustering problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to cluster the samples into their respective subspaces and remove possible outliers as well...
 
Image Super-Resolution via Low-Pass Filter Based Multi-scale Image Decomposition
Found in: 2012 IEEE International Conference on Multimedia and Expo (ICME)
By Shuyuan Zhu,Bing Zeng,Shuicheng Yan
Issue Date:July 2012
pp. 1045-1050
This paper presents a spatial-varying minimum mean square error (MMSE)-based approach to construct super-resolution images from single source image of a lower resolution. The unique feature of this approach is that it works on a set of sub-images (also cal...
 
Robust Non-negative Graph Embedding: Towards noisy data, unreliable graphs, and noisy labels
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Hanwang Zhang, Zheng-Jun Zha, Shuicheng Yan, Meng Wang, Tat-Seng Chua
Issue Date:June 2012
pp. 2464-2471
Non-negative data factorization has been widely used recently. However, existing techniques, such as Non-negative Graph Embedding (NGE), often suffer from noisy data, unreliable graphs, and noisy labels, which are commonly encountered in real-world applica...
 
Efficient structure detection via random consensus graph
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Hairong Liu, Shuicheng Yan
Issue Date:June 2012
pp. 574-581
In this paper, we propose an efficient method to detect the underlying structures in data. The same as RANSAC, we randomly sample MSSs (minimal size samples) and generate hypotheses. Instead of analyzing each hypothesis separately, the consensus informatio...
 
Practical low-rank matrix approximation under robust L1-norm
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Yinqiang Zheng, Guangcan Liu,S. Sugimoto, Shuicheng Yan,M. Okutomi
Issue Date:June 2012
pp. 1410-1417
A great variety of computer vision tasks, such as rigid/nonrigid structure from motion and photometric stereo, can be unified into the problem of approximating a low-rank data matrix in the presence of missing data and outliers. To improve robustness, the ...
 
Generalizing Wiberg algorithm for rigid and nonrigid factorizations with missing components and metric constraints
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Yinqiang Zheng,S. Sugimoto, Shuicheng Yan,M. Okutomi
Issue Date:June 2012
pp. 2010-2017
In spite of intensive endeavor over decades, rigid and nonrigid factorizations under metric constraints, possibly in the presence of missing components, remain to be very challenging. In this work, we try to break the hard nut by generalizing to these prob...
 
Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Si Liu, Zheng Song, Guangcan Liu, Changsheng Xu, Hanqing Lu, Shuicheng Yan
Issue Date:June 2012
pp. 3330-3337
In this paper, we address a practical problem of cross-scenario clothing retrieval - given a daily human photo captured in general environment, e.g., on street, finding similar clothing in online shops, where the photos are captured more professionally and...
 
Hierarchical matching with side information for image classification
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Qiang Chen, Zheng Song, Yang Hua, Zhongyang Huang, Shuicheng Yan
Issue Date:June 2012
pp. 3426-3433
In this work, we introduce a hierarchical matching framework with so-called side information for image classification based on bag-of-words representation. Each image is expressed as a bag of orderless pairs, each of which includes a local feature vector e...
 
Omni-range spatial contexts for visual classification
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Bingbing Ni, Mengdi Xu, Jinhui Tang, Shuicheng Yan,P. Moulin
Issue Date:June 2012
pp. 3514-3521
Spatial contexts encode rich discriminative information for visual classification. However, as object shapes and scales vary significantly among images, spatial contexts with manually specified distance ranges are not guaranteed with optimality. In this wo...
 
Towards Optimal Discriminating Order for Multiclass Classification
Found in: Data Mining, IEEE International Conference on
By Dong Liu,Shuicheng Yan,Yadong Mu,Xian-Sheng Hua,Shih-Fu Chang,Hong-Jiang Zhang
Issue Date:December 2011
pp. 388-397
In this paper, we investigate how to design an optimized discriminating order for boosting multiclass classification. The main idea is to optimize a binary tree architecture, referred to as Sequential Discriminating Tree (SDT), that performs the multiclass...
 
Multi-task low-rank affinity pursuit for image segmentation
Found in: Computer Vision, IEEE International Conference on
By Bin Cheng,Guangcan Liu,Jingdong Wang, Zhongyang Huang,Shuicheng Yan
Issue Date:November 2011
pp. 2439-2446
This paper investigates how to boost region-based image segmentation by pursuing a new solution to fuse multiple types of image features. A collaborative image segmentation framework, called multi-task low-rank affinity pursuit, is presented for such a pur...
 
Latent Low-Rank Representation for subspace segmentation and feature extraction
Found in: Computer Vision, IEEE International Conference on
By Guangcan Liu,Shuicheng Yan
Issue Date:November 2011
pp. 1615-1622
Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dictionary, which is a key aspect of LRR. However, such a strategy may depress t...
 
Multi-label visual classification with label exclusive context
Found in: Computer Vision, IEEE International Conference on
By Xiangyu Chen,Xiao-Tong Yuan,Qiang Chen,Shuicheng Yan,Tat-Seng Chua
Issue Date:November 2011
pp. 834-841
We introduce in this paper a novel approach to multi-label image classification which incorporates a new type of context -- label exclusive context -- with linear representation and classification. Given a set of exclusive label groups that describe the ne...
 
Learning universal multi-view age estimator using video context
Found in: Computer Vision, IEEE International Conference on
By Zheng Song,Bingbing Ni,Dong Guo,Terence Sim,Shuicheng Yan
Issue Date:November 2011
pp. 241-248
Many existing techniques for analyzing face images assume that the faces are at nearly frontal. Generalizing to non-frontal faces is often difficult, due to a dearth of ground truth for non-frontal faces and also to the inherent challenges in handling pose...
 
Multi-class semi-supervised SVMs with Positiveness Exclusive Regularization
Found in: Computer Vision, IEEE International Conference on
By Xiaobai Liu,Xiaotong Yuan,Shuicheng Yan, Hai Jin
Issue Date:November 2011
pp. 1435-1442
In this work, we address the problem of multi-class classification problem in semi-supervised setting. A regularized multi-task learning approach is presented to train multiple binary-class Semi-Supervised Support Vector Machines (S3VMs) using the one-vs-r...
 
Predicting occupation via human clothing and contexts
Found in: Computer Vision, IEEE International Conference on
By Zheng Song, Meng Wang, Xian-sheng Hua,Shuicheng Yan
Issue Date:November 2011
pp. 1084-1091
Predicting human occupations in photos has great application potentials in intelligent services and systems. However, using traditional classification methods cannot reliably distinguish different occupations due to the complex relations between occupation...
 
Contextualizing object detection and classification
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Zheng Song, Qiang Chen, Zhongyang Huang, Yang Hua, Shuicheng Yan
Issue Date:June 2011
pp. 1585-1592
In this paper, we investigate how to iteratively and mutually boost object classification and detection by taking the outputs from one task as the context of the other one. First, instead of intuitive feature and context concatenation or postprocessing wit...
 
Geometric $/ell$_p-norm feature pooling for image classification
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Jiashi Feng, Bingbing Ni, Qi Tian, Shuicheng Yan
Issue Date:June 2011
pp. 2609-2704
Modern visual classification models generally include a feature pooling step, which aggregates local features over the region of interest into a statistic through a certain spatial pooling operation. Two commonly used operations are the average and max poo...
 
Segment an image by looking into an image corpus
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Xiaobai Liu, Jiashi Feng, Shuicheng Yan, Liang Lin, Hai Jin
Issue Date:June 2011
pp. 2249-2256
This paper investigates how to segment an image into semantic regions by harnessing an unlabeled image corpus. First, the image segmentation task is recast as a small-size patch grouping problem. Then, we discover two novel patch-pair priors, namely the fi...
 
Accelerated low-rank visual recovery by random projection
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Yadong Mu, Jian Dong, Xiaotong Yuan, Shuicheng Yan
Issue Date:June 2011
pp. 2609-2616
Exact recovery from contaminated visual data plays an important role in various tasks. By assuming the observed data matrix as the addition of a low-rank matrix and a sparse matrix, theoretic guarantee exists under mild conditions for exact data recovery. ...
 
Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees
Found in: Data Mining Workshops, International Conference on
By Yuzhao Ni, Ju Sun, Xiaotong Yuan, Shuicheng Yan, Loong-Fah Cheong
Issue Date:December 2010
pp. 1179-1188
Recently there is a line of research work proposing to employ Spectral Clustering (SC) to segment (group)\footnote{Throughout the paper, we use segmentation, clustering, and grouping, and their verb forms, interchangeably.} high-dimensional structural data...
 
Compact Codebook Generation Towards Scale-Invariance
Found in: Image and Video Technology, Pacific-Rim Symposium on
By Si Liu, Shuicheng Yan, Changsheng Xu, Hanqing Lu
Issue Date:November 2010
pp. 376-380
In this paper, we present a novel visual codebook learning approach towards compactness and scale-invariance for dense patch image encoding. Firstly, each image is described as a bag of orderless gridding local patches, each of which is expressed in three ...
 
Learning a Propagable Graph for Semisupervised Learning: Classification and Regression
Found in: IEEE Transactions on Knowledge and Data Engineering
By Bingbing Ni,Shuicheng Yan,Ashraf A. Kassim
Issue Date:January 2012
pp. 114-126
In this paper, we present a novel framework, called learning by propagability, for two essential data mining tasks, i.e., classification and regression. The whole learning process is driven by the philosophy that the data labels and the optimal feature rep...
 
Activity recognition using dense long-duration trajectories
Found in: Multimedia and Expo, IEEE International Conference on
By Ju Sun, Yadong Mu, Shuicheng Yan, Loong-Fah Cheong
Issue Date:July 2010
pp. 322-327
Current research on visual action/activity analysis has mostly exploited appearance-based static feature descriptions, plus statistics of short-range motion fields. The deliberate ignorance of dense, long-duration motion trajectories as features is largely...
 
Automated assembly of shredded pieces from multiple photos
Found in: Multimedia and Expo, IEEE International Conference on
By Shengjiao Cao, Hairong Liu, Shuicheng Yan
Issue Date:July 2010
pp. 358-363
In this paper, we investigate the problem of automated assembly of shredded pieces from multiple photos. We first establish candidate matchings between fragments by using both shape and appearance information. A weighted graph whose vertices represent shre...
 
Spatialized epitome and its applications
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Xinqi Chu, Shuicheng Yan, Liyuan Li, Kap Luk Chan, Thomas S. Huang
Issue Date:June 2010
pp. 311-318
Due to the lack of explicit spatial consideration, existing epitome model may fail for image recognition and target detection, which directly motivates us to propose the so-called spatialized epitome in this paper. Extended from the original graphical mode...
 
Weakly-supervised hashing in kernel space
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Yadong Mu, Jialie Shen, Shuicheng Yan
Issue Date:June 2010
pp. 3344-3351
The explosive growth of the vision data motivates the recent studies on efficient data indexing methods such as locality-sensitive hashing (LSH). Most existing approaches perform hashing in an unsupervised way. In this paper we move one step forward and pr...
 
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