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IEEE Workshop on Multimedia Signal Processing

作者： Wei Lei Ruiqin Xiong Siwei Ma Luhong Liang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Institute of Digital Media Peking University Beijing China

In image/video processing software and hardware products, low complexity interpolation algorithms, such as cubic and splines methods, are commonly used. However, these methods tend to blur textures and produce jaggy effect compared with other adaptive methods such as NEDI, SAI. Tanner graph based image interpolation algorithm has better effect in dealing with edge and texture, but with high computation complexity. Thanks to the high performance parallel processing capability of today's GPU, use of complex algorithms for real time application is becoming possible. In this paper, we present a fast algorithm for tanner graph based image interpolation and it's implementation on GPU. In our algorithm, the image model training process of tanner graph based image interpolation is greatly simplified. Experimental results show that the GPU implementation can be more than 47 times as fast as the CPU implementation.

关键词： Graphics processing unit Interpolation Training Instruction sets Parallel processing Equations Image resolution

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Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop

Scaling-Up Item-Based Collaborative Filtering Recommendation...

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IEEE World Congress on Services (SERVICES)

作者： Jing Jiang Jie Lu Guangquan Zhang Guodong Long Decision Systems & E-Service Intelligence Research Laboratory Center of Quantum Computing and Intelligent System School of Software Faculty of Engineering and Information Technology Sydney University of Technology Sydney NSW Australia

Collaborative filtering (CF) techniques have achieved widespread success in E-commerce nowadays. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender systems in which high quality recommendations are required and more recommendations per second for millions of customers and products need to be performed. Thus, the improvement of scalability and efficiency of collaborative filtering (CF) algorithms become increasingly important and difficult. In this paper, we developed and implemented a scaling-up item-based collaborative filtering algorithm on MapReduce, by splitting the three most costly computations in the proposed algorithm into four Map-Reduce phases, each of which can be independently executed on different nodes in parallel. We also proposed efficient partition strategies not only to enable the parallel computation in each Map-Reduce phase but also to maximize data locality to minimize the communication cost. Experimental results effectively showed the good performance in scalability and efficiency of the item-based CF algorithm on a Hadoop cluster.

关键词： Scalability Prediction algorithms Algorithm design and analysis Collaboration Filtering Partitioning algorithms Clustering algorithms

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Visual saliency detection by spatially weighted dissimilarity

Visual saliency detection by spatially weighted dissimilarit...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lijuan Duan Chunpeng Wu Jun Miao Laiyun Qing Yu Fu College of Computer Science and Technology Beijing University of Technology Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China School of Information Science and Engineering Chinese Academy and Sciences Beijing China Department of Computing University of Surrey Guildford Surrey UK

In this paper, a new visual saliency detection method is proposed based on the spatially weighted dissimilarity. We measured the saliency by integrating three elements as follows: the dissimilarities between image patches, which were evaluated in the reduced dimensional space, the spatial distance between image patches and the central bias. The dissimilarities were inversely weighted based on the corresponding spatial distance. A weighting mechanism, indicating a bias for human fixations to the center of the image, was employed. The principal component analysis (PCA) was the dimension reducing method used in our system. We extracted the principal components (PCs) by sampling the patches from the current image. Our method was compared with four saliency detection approaches using three image datasets. Experimental results show that our method outperforms current state-of-the-art methods on predicting human fixations.

关键词： Humans Color Visualization Image color analysis Principal component analysis Correlation Computational modeling

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Multi-kernel Co-SVM with a new strategy using unlabeled data for image retrieval

Multi-kernel Co-SVM with a new strategy using unlabeled data...

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International Conference on Pervasive computing and Applications (ICPCA)

作者： Li Zongmin Liu Yang Li Hua School of Computer and Communications Engineering China University of Petroleum Shandong China Key Laboratory Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781457702099

Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job in solving these problems but still has some flaws. We propose three strategies to try to improve this algorithm: (1) different kernel functions are used to characterize the color and texture visual similarities; (2) a new method is proposed to caculate the confident scores of the contention samples; (3) a bunch of the most irrelevant images with the highest confident score are added into the labeled images to extend the size of labeled data while choosing a bunch of images for user labeling. Experimental results verify the superiority of our method over Co-SVM.

关键词： Support vector machines Multimedia communication Classification algorithms

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A novel coarse-to-fine hair segmentation method

A novel coarse-to-fine hair segmentation method

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International Conference on Automatic Face and Gesture Recognition

作者： Dan Wang Xiujuan Chai Hongming Zhang Hong Chang Wei Zeng Shiguang Shan Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China NEC Laboratories Beijing China

Segmenting hair regions from human images facilitates many tasks like hair synthesis and hair style trends forecast. However, hair segmentation is quite challenging due to hair/background confusion and large hair pattern diversity. To address these problems to some extent, this paper proposes a novel coarse-to-fine hair segmentation method. In our approach, firstly, the recently proposed “Active Segmentation with Fixation” (ASF) is used to coarsely define an enclosed candidate region with high-recall (but possibly low-precision) of hair pixels and exclude considerable part of the backgrounds which are easily confused with hair. Then Graph Cuts (GC) method is applied to the candidate regions to remove additional false positives by incorporating hair-specific information. Specifically, Bayesian method is employed to select some reliable hair and background regions (seeds) among the ones over-segmented by Mean Shift. SVM classifier is then learnt online from these seeds and explored to predict hair/background likelihood probability, which is subsequently fed into GC algorithm. The novelty of the proposed approach lies in three folds: 1) an elaborate design of hair segmentation framework, which utilizes ASF to reduce the candidate hair regions and adopts GC to achieve more accurate hair region contours; 2) the region-based strategy for seed selection; 3) the exploration of the discriminative method, SVM, to predict the probability of each pixel belonging to hair and background regions. Extensive experimental results demonstrate the approach outperforms recently proposed methods.

关键词： Hair Pixel Image segmentation Support vector machines Bayesian methods Image color analysis Databases

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Long term bank failure prediction using Fuzzy Refinement-based Transductive Transfer learning

Long term bank failure prediction using Fuzzy Refinement-bas...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Vahid Behbood Jie Lu Guangquan Zhang Decision Systems & E-Service Intelligence Research Laboratory Centre of Qua ntum Computing and Intelligent System School of Software Faculty of Engineering and Information Technology University of Technology-Sydney Broadway NSW Australia

Machine learning algorithms, which have been considered as robust methods in different computational fields, assume that the training and test data are drawn from the same distribution. This assumption may be violated in many real world applications like bank failure prediction because training and test data may come from different time periods or domains. An efficient novel algorithm known as Fuzzy Refinement (FR) is proposed in this paper to solve this problem and improve the performance. The algorithm utilizes the fuzzy system and similarity concept to modify the instances' labels in target domain which was initially predicted by shift-unaware Fuzzy Neural Network (FNN) proposed by [1]. The experiments are performed using bank failure financial data of United States to evaluate the algorithm performance. The results address a significant improvement in the predictive accuracy of FNN due to applying the proposed algorithm.

关键词： Prediction algorithms Fuzzy neural networks Pragmatics Training Inference algorithms Accuracy Machine learning algorithms

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A NOVEL FRAMEWORK OF ONLINE NETWORK CODING FOR MULTICAST SWITCHES WITH CONSTRAINED BUFFERS

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Journal of Electronics(China) 2011年第4期28卷 460-467页

作者： Wang Wei Yu Li Zhu Guangxi Li Hui Wuhan National Laboratory for Optoelectronics Division of Communiction ＆ Intelligent NetworksDepartment of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 China Shenzhen Key Lab of Cloud Computing Technology ＆ Application Shenzhen Graduate School Peking University Shenzhen 518055 China

Network coding is able to address output conflicts when fanout splitting is allowed for multicast ***,it successfully achieves a larger rate region than non-coding approaches in crossbar ***,network coding requires large coding buffers and a high computational cost on encoding and *** this paper,we propose a novel Online Network Coding framework called Online NC for multicast switches,which is adaptive to constrained ***,it enjoys a much lower decoding complexity by a Vandermonde matrix based approach,as compared to conven-tional randomized network coding Our approach realizes online coding with one coding algo-rithm that synchronizes buffering and ***,we significantly reduce requirements on buffer space,while also sustaining high *** confirm the superior advantages of our contributions using empirical studies.

关键词： Packet switching Multicast Network coding Vandermonde matrix

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Attention driven face recognition: A combination of spatial variant fixations and glance

Attention driven face recognition: A combination of spatial ...

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International Conference on Automatic Face and Gesture Recognition

作者： Chongxiu Wang Laiyun Qing Jun Miao Fang Fang Xilin Chen University of the Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China

This paper proposes a novel face recognition algorithm inspired by the selective attention of Human Visual System (HVS). We record four observers' eye movements when they are viewing 100 FRGC [1] frontal view face images and find that the observers are highly consistent in the regions fixated. Inspired by the fact that fovea of HVS has a much higher spatial acuity than the periphery, a face recognition algorithm based on spatial variant sampling is proposed to simulate such foveated imaging phenomenon, where more information is reserved for the fixated regions. Moreover, information extracted from glance which adopts the low spatial frequency components of the image is integrated into the face recognition system to elicit a percept that occurs before any fixations. The experimental results on FERET database [2] demonstrate that the proposed method not only reduces the computational cost, but also achieves comparable performance, which shows that the characteristics of the HVS provide valuable insights into face recognition.

关键词： Face Face recognition Observers Humans Pixel Databases Feature extraction

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Shape-based web image clustering for unsupervised object detection?

Shape-based web image clustering for unsupervised object det...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wei Zheng Changhu Wang Xilin Chen Graduate School Chinese Academy and Sciences Beijing China Microsoft Research Asia Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781612843483

Automatic object detection for an arbitrary class is an important but very challenging problem, due to the countless kinds of objects in the world and the large amount of labeling work for each object. In this work, we target at solving the problem of automatic object detection for an arbitrary class without the laborious human effort. Motivated by the explosive growth of Web images and the phenomenal success of search techniques, we develop an unsupervised object detection framework by automatically training the object detector on the top returns of certain image search engine queried by the name of the object class. In order to automatically isolate the objects from the Web images for training, only clipart images with simple background are used, which preserve most of the shape information of the objects. A two-stage shape-based clustering algorithm is proposed to mine typical shapes of the object, in which the inner-class variance of object shapes is considered and undesired images are filtered out. In order to reduce the gap between clipart images and real-world images, we introduce an efficient algorithm to synthesize the real-world images from clipart images, and only shape feature is used in the detector training part. Finally, the synthetic images could be used to train object detectors by an off-the-shelf discriminative algorithm, e.g., boosting or SVM. Extensive experiments show the effectiveness of the proposed framework on objects with simple and representative shapes, and the proposed framework could be considered as a good beginning of solving this challenging problem.

关键词： Shape Clustering algorithms Training Detectors Image edge detection Object detection Image color analysis

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Context constrained facial landmark localization based on discontinuous Haar-like feature

Context constrained facial landmark localization based on di...

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International Conference on Automatic Face and Gesture Recognition

作者： Xiaowei Zhao Xiujuan Chai Zhiheng Niu Cherkeng Heng Shiguang Shan Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Panasonic Singapore Laboratories Private Limited (PSL) Singapore

Facial landmark localization is well known as one of the bottlenecks in face recognition. This paper proposes a novel facial landmark localization method, which introduces facial context constrains into cascaded AdaBoost framework. The motivation of our method lies in the basic human physiology observation that not only the local texture information but also the global context information is used together for human to realize the landmark location task. Therefore, in our solution, a novel type of Haar-like feature, called discontinuous Haar-like feature, is proposed to characterize the facial context, i.e. the cooccurrence relationship between target facial landmark and other local texture patterns within face region (including other landmarks, facial organs and also smoothing regions). For the locating task, traditional Haar-like features (characterizing local texture information) and discontinuous Haar-like features (characterizing context constrains in global sense) are combined together to form more powerful representations. Through Real AdaBoost learning, distinctive features are selected automatically and used for facial landmark detection. Our experiments on BioID and Cohn-Kanade databases have validated the proposed method by comparing with other state-of-the-art results.

关键词： Context Face Feature extraction Mouth Databases Pixel Accuracy

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