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Document summarization based on data reconstruction 26

Document summarization based on data reconstruction

引用

26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： He, Zhanying Chen, Chun Bu, Jiajun Wang, Can Zhang, Lijun Cai, Deng He, Xiaofei Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou 310027 China State Key Lab. of CAD and CG College of Computer Science Zhejiang University Hangzhou 310058 China

ISBN: (纸本)9781577355687

Document summarization is of great value to many real world applications, such as snippets generation for search results and news headlines generation. Traditionally, document summarization is implemented by extracting sentences that cover the main topics of a document with a minimum redundancy. In this paper, we take a different perspective from data reconstruction and propose a novel framework named Document Summarization based on Data Reconstruction (DSDR). Specifically, our approach generates a summary which consist of those sentences that can best reconstruct the original document. To model the relationship among sentences, we introduce two objective functions: (1) linear reconstruction, which approximates the document by linear combinations of the selected sentences;(2) nonnegative linear reconstruction, which allows only additive, not subtractive, linear combinations. In this framework, the reconstruction error becomes a natural criterion for measuring the quality of the summary. For each objective function, we develop an efficient algorithm to solve the corresponding optimization problem. Extensive experiments on summarization benchmark data sets DUC 2006 and DUC 2007 demonstrate the effectiveness of our proposed approach. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Data mining

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Learning visual saliency based on object's relative relationship

Learning visual saliency based on object's relative relation...

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19th International Conference on Neural Information Processing, ICONIP 2012

作者： Wang, Senlin Zhao, Qi Song, Mingli Bu, Jiajun Chen, Chun Tao, Dacheng Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou 310027 China Department of Electrical and Computer Engineering NUS Singapore Centre for Quantum Computation and Information Systems UTS Australia

ISBN: (纸本)9783642344992

As a challenging issue in both computer vision and psychological research, visual attention has arouse a wide range of discussions and studies in recent years. However, conventional computational models mainly focus on low-level information, while high-level information and their interrelationship are ignored. In this paper, we stress the issue of relative relationship between high-level information, and a saliency model based on low-level and high-level analysis is also proposed. Firstly, more than 50 categories of objects are selected from nearly 800 images in MIT data set[1], and concrete quantitative relationship is learned based on detail analysis and computation. Secondly, using the least square regression with constraints method, we demonstrate an optimal saliency model to produce saliency maps. Experimental results indicate that our model outperforms several state-of-art methods and produces better matching to human eye-tracking data. © 2012 Springer-Verlag.

关键词： Eye tracking

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Efficient online learning for large-scale sparse Kernel logistic regression 26

Efficient online learning for large-scale sparse Kernel logi...

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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： Zhang, Lijun Jin, Rong Chen, Chun Bu, Jiajun He, Xiaofei Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou 310027 China Department of Computer Science and Engineering Michigan State University East Lansing MI 48824 United States State Key Lab of CADandCG College of Computer Science Zhejiang University Hangzhou 310058 China

ISBN: (纸本)9781577355687

In this paper, we study the problem of large-scale Kernel Logistic Regression (KLR). A straightforward approach is to apply stochastic approximation to KLR. We refer to this approach as non-conservative online learning algorithm because it updates the kernel classifier after every received training example, leading to a dense classifier. To improve the sparsity of the KLR classifier, we propose two conservative online learning algorithms that update the classifier in a stochastic manner and generate sparse solutions. With appropriately designed updating strategies, our analysis shows that the two conservative algorithms enjoy similar theoretical guarantee as that of the non-conservative algorithm. Empirical studies on several benchmark data sets demonstrate that compared to batch-mode algorithms for KLR, the proposed conservative online learning algorithms are able to produce sparse KLR classifiers, and achieve similar classification accuracy but with significantly shorter training time. Furthermore, both the sparsity and classification accuracy of our methods are comparable to those of the online kernel SVM. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Stochastic systems

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Multiple kernel learning from noisy labels by stochastic programming

Multiple kernel learning from noisy labels by stochastic pro...

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29th International Conference on Machine Learning, ICML 2012

作者： Yang, Tianbao Mahdavi, Mehrdad Jin, Rong Zhang, Lijun Zhou, Yang Department of Computer Science and Engineering Michigan State University East Lansing MI 48824 United States Zhejiang Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China Yahoo Labs Santa Clara CA 95054 United States

ISBN: (纸本)9781450312851

We study the problem of multiple kernel learning from noisy labels. This is in contrast to most of the previous studies on multiple kernel learning that mainly focus on developing efficient algorithms and assume perfectly labeled training examples. Directly applying the existing multiple kernel learning algorithms to noisily labeled examples often leads to suboptimal performance due to the incorrect class assignments. We address this challenge by casting multiple kernel learning from noisy labels into a stochastic programming problem, and presenting a minimax formulation. We develop an efficient algorithm for solving the related convex-concave optimization problem with a fast convergence rate of O(I/T) where T is the number of iterations. Empirical studies on UCI data sets verify both the effectiveness and the efficiency of the proposed algorithm. Copyright 2012 by the author(s)/owner(s).

关键词： Stochastic programming

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Spatial graphlet matching kernel for recognizing aerial image categories

Spatial graphlet matching kernel for recognizing aerial imag...

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International Conference on Pattern Recognition

作者： Luming Zhang Mingli Song Li Sun Xiao Liu Yinting Wang Dacheng Tao Jiajun Bu Chun Chen Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China Centre for Quantum Computation and Information Systems UTS USA

This paper presents a method for recognizing aerial image categories based on matching graphlets(i.e., small connected subgraphs) extracted from aerial images. By constructing a Region Adjacency Graph (RAG) to encode the geometric property and the color distribution of each aerial image, we cast aerial image category recognition as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by matching all their respective graphlets. Towards an effective graphlet matching process, we develop a manifold embedding algorithm to transfer different-sized graphlets into equal length feature vectors and further integrate these feature vectors into a kernel. This kernel is used to train a SVM [8] classifier for aerial image categories recognition. Experimental results demonstrate our method outperforms several state-of-the-art object/scene recognition models.

关键词： Image recognition Kernel Vectors Manifolds Support vector machines Image segmentation Training

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Joint shot boundary detection and key frame extraction

Joint shot boundary detection and key frame extraction

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International Conference on Pattern Recognition

作者： Xiao Liu Mingli Song Luming Zhang Senlin Wang Jiajun Bu Chun Chen Dacheng Tao Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China Center for Quantum Computation and Information Systems UTS USA

ISBN: (纸本)9781467322164

Representing a video by a set of key frames is useful for efficient video browsing and retrieving. But key frame extraction keeps a challenge in the computer vision field. In this paper, we propose a joint framework to integrate both shot boundary detection and key frame extraction, wherein three probabilistic components are taken into account, i.e. the prior of the key frames, the conditional probability of shot boundaries and the conditional probability of each video frame. Thus the key frame extraction is treated as a Maximum A Posteriori which can be solved by adopting alternate strategy. Experimental results show that the proposed method preserves the scene level structure and extracts key frames that are representative and discriminative.

关键词： Joints Probabilistic logic Surveillance Histograms Accuracy Feature extraction Data mining

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Detecting discontinuities for surface reconstruction

Detecting discontinuities for surface reconstruction

引用

International Conference on Pattern Recognition

作者： Yinting Wang Jiajun Bu Na Li Mingli Song Ping Tan Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China Zhejiang International Studies University China National University of Singapore Singapore

ISBN: (纸本)9781467322164

Photometric stereo algorithms produce a map of normal directions from the input images. The 3D surface can be reconstructed from this normal map. Existing surface reconstruction works often assume the normal map is integrable but contaminated by small scale non-integrable noise. However, real surfaces often contain large discontinuities such as occlusion boundaries and sharp depth changes, which break the integrable assumption commonly made in many works. Here, we propose a method to detect these discontinuities by combining multiple geometric cues with trained classifiers and a simple graph optimization. The surface is then reconstructed with the guidance of these detected discontinuities. Experiments show our method outperforms existing works.

关键词： Image edge detection Surface reconstruction Image reconstruction Cameras Shape Educational institutions Face

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Large-scale outdoor scene classification by boosting a set of highly discriminative and low redundant graphlets

Large-scale outdoor scene classification by boosting a set o...

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11th IEEE International Conference on Data Mining Workshops, ICDMW 2011

作者： Zhang, Luming Song, Mingli Deng, Xiaoyu Bu, Jiajun Chen, Chun Zhejiang Provincial Key Laboratory of Service Robot Computer Science College Zhejiang University China

ISBN: (纸本)9780769544090

Large-scale outdoor scene classification is an important issue in multimedia information retrieval. In this paper, we propose an efficient scene classification model by integrating outdoor scene image's local features into a set of highly discriminative and less redundant graphlets (i.e., small connected subgraph). Firstly, each outdoor scene image is segmented into a number of regions in terms of its color intensity distribution. And a region adjacency graph (RAG) is defined to encode the geometric property and color intensity distribution of outdoor scene image. Then, the frequent sub-structures are mined statistically from the RAGs corresponding to the training outdoor scene images. And a selecting process is carried out to obtain a set of sub-structures from the frequent ones towards being highly discriminative and low redundant. And these selected substructures are used as templates to extract the corresponding graphlets. Finally, we integrate these extracted graphlets by a multi-class boosting strategy for outdoor scene classification. The experimental results on the challenging SUN [1] data set and the LHI [14] data set validate the effectiveness of our approach. © 2011 IEEE.

关键词： Classification (of information)

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Social recommendation using low-rank semidefinite program 25

Social recommendation using low-rank semidefinite program

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25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11

作者： Zhu, Jianke Ma, Hao Chen, Chun Bu, Jiajun Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China Microsoft Research United States

ISBN: (纸本)9781577355083

The most critical challenge for the recommendation system is to achieve the high prediction quality on the large scale sparse data contributed by the users. In this paper, we present a novel approach to the social recommendation problem, which takes the advantage of the graph Laplacian regularization to capture the underlying social relationship among the users. Differently from the previous approaches, that are based on the conventional gradient descent optimization, we formulate the presented graph Laplacian regularized social recommendation problem into a low-rank semidefinite program, which is able to be efficiently solved by the quasi-Newton algorithm. We have conducted the empirical evaluation on a large scale dataset of high sparsity, the promising experimental results show that our method is very effective and efficient for the social recommendation task. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Laplace transforms

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Jumping scanning path error diffusion: A novel halftoning algorithm improving mid-tone quality

Jumping scanning path error diffusion: A novel halftoning al...

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International Workshop on Digital Media and Digital Content Management, DMDCM 2011

作者： Zhou, Yan Chen, Chun Wang, Qiang Bu, Jiajun Zhejiang Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou 310027 China

ISBN: (纸本)9780769544137

In this paper, we describe a novel error diffusion scheme for higher halftone quality with less visual artifacts. The proposed algorithm improves mid-tone quality of error diffusion significantly by diffusing the error along a jumping scanning path. The algorithm calculates the accumulative error to determine the point where breaks up the scanning path. A cost function is developed to search the optimal threshold of the accumulative error. The cost function helps to bring the Fourier spectra as close as possible to the corresponding "blue noise" spectra, and minimize the anisotropy. Experiments show that compared with existing error diffusion algorithms that use continuous scanning path, the new algorithm can remove visual anomalies more effectively. Fourier analysis of experimental results further supports this conclusion. © 2011 IEEE.

关键词： Cost functions

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