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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Hong Lu Bohong Yang Rui Zhao Pengliang Qu Wenqiang Zhang Shanghai Key Lab. of Intelligent Information Processing Fudan University P. R. China Shanghai Engineering Research Center for Video Technology and System Fudan University P. R. China

ISBN: (纸本)9781479976478

We propose in this paper an intelligent system for human fall detection in indoor environments. It can serve as home surveillance for elderly person. Specifically, we detect the human body captured by camera using the Vibe algorithm. Then Gabor feature of a human body is extracted as observation feature. Based on the extracted feature, incidents are detected as the changes from the standing state to the fall state in the feature space. The feature is detected on single image and is effective and efficient. Compared with the motion features across images combined with biological inspired feed-forward network, our method can obtain more robust detection results.

关键词： Feature extraction Senior citizens Conferences Support vector machines Robustness Surveillance Visualization

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Twin support vector machines based on Quantum particle swarm optimization

Journal of Software

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Journal of Software 2013年第7期8卷 1743-1750页

作者： Ding, Shifei Wu, Fulin Nie, Ru Yu, Junzhao Huang, Huajuan School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Twin Support Vector Machines (TWSVM) are developed on the basis of Proximal Support Vector Machines (PSVM) and Proximal Support Vector Machine based on the generalized eigenvalues(GEPSVM). The solving of binary classification problem is converted to the solving of two smaller quadratic programming problems by TWSVM. And then it gets two non-parallel hyperplanes. Its efficiency of dealing with the problems and performance are better than the traditional support vector machines. However, it also has some problems. Its own parameters are difficult to be appointed. In order to solve this problem, on the basis of in-depth study of TWSVM, this paper proposes an algorithm that is the Twin Support Vector Machines based on Quantum Particle Swarm Optimization (QPSO-TWSVM).By the use of the global searching ability of the Quantum Particle Swarm Optimization (QPSO), QPSO-TWSVM can search the optimal parameters in the global scope and avoid itself falling into the local optimum prematurely to find the values of the parameters which are the closest to the optimal parameters. QPSO-TWSVM avoids using the empirical values to appoint the parameters successfully. Compared with the traditional TWSVM, QPSO-TWSVM can appoint the parameters more accurately and avoid selecting the parameters blindly. Because of the better parameter selections, QPSO-TWSVM improves the classification accuracy of TWSVM. © 2013 ACADEMY PUBLISHER.

关键词： Support vector machines

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A feature weighted spectral clustering algorithm based on knowledge entropy

Journal of Software

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Journal of Software 2013年第5期8卷 1101-1108页

作者： Jia, Hongjie Ding, Shifei Zhu, Hong Wu, Fulin Bao, Lina School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Spectral clustering has aroused extensive attention in recent years. It performs well for the data with arbitrary shape and can converge to global optimum. But traditional spectral clustering algorithms set the importance of all attributes to 1 as default, when measuring the similarity of data points. In fact, each attribute contains different information and their contributions to the clustering are also different. In order to make full use of the information contained in each attribute and weaken the interference of noise data or redundant attributes, this paper proposes a feature weighted spectral clustering algorithm based on knowledge entropy (FWKE-SC). This algorithm uses the concept of knowledge entropy in rough set to evaluate the importance of each attribute, which can be used as the attribute weights, and then applies spectral clustering method to cluster the data points. Experiments show that FWKE-SC algorithm deals with high-dimensional data very well and has better robustness and generalization ability. © 2013 ACADEMY PUBLISHER.

关键词： Entropy

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ObjectSense: A scalab.e multi-objects recognition system based on partial-duplicate image retrieval 13

ObjectSense: A scalable multi-objects recognition system bas...

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3rd ACM International Conference on Multimedia Retrieval, ICMR 2013

作者： Wang, Shuang Xue, Yunfeng Chu, Lingyang Jiang, Yuhao Jiang, Shuqiang Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS Beijing China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781450320337

In this demo, we present ObjectSense, a scalab.e object recognition system that recognizes multiple objects present in a static image or in the camera frames. Instead of applying learning based recognition framework, this system identifies objects through Partial-Duplicate Image Retrieval (PDIR) based method. First, objects are identified by measuring the similarity between an incoming image and reference image corpus that are lab.led with the objects. To compute image similarities, we explore the Consistency Graph Model (CGM), which robustly rejects spatially inconsistent feature matches with the advantage of orientations and positions of local features. Then a kNN voting method is used to decide the object category based on the quantized image similarities. ObjectSense is scalab.e with promisingly high recall and accuracy, which fits well into recognition-guided shopping and human computer interaction. We built ObjectSense on two platforms, PC and Android. © 2013 Authors.

关键词： Object recognition

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Are school-of-thought words characterizable?

Are school-of-thought words characterizable?

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Jiang, Xiaorui Sun, Xiaoping Zhuge, Hai Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Nanjing University of Posts and Telecommunications Nanjing China Aston University Birmingham United Kingdom

ISBN: (纸本)9781937284510

School of thought analysis is an important yet not-well-elab.rated scientific knowledge discovery task. This paper makes the first attempt at this problem. We focus on one aspect of the problem: do characteristic school-of-thought words exist and whether they are characterizable? To answer these questions, we propose a probabilistic generative School-Of-Thought (SOT) model to simulate the scientific authoring process based on several assumptions. SOT defines a school of thought as a distribution of topics and assumes that authors determine the school of thought for each sentence before choosing words to deliver scientific ideas. SOT distinguishes between two types of school-of-thought words for either the general background of a school of thought or the original ideas each paper contributes to its school of thought. Narrative and quantitative experiments show positive and promising results to the questions raised above © 2013 Association for Computational Linguistics. © 2013 Association for Computational Linguistics.

关键词： Data mining

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Parametric local multimodal hashing for cross-view similarity search

Parametric local multimodal hashing for cross-view similarit...

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23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Zhai, Deming Chang, Hong Zhen, Yi Liu, Xianming Chen, Xilin Gao, Wen School of Computer Science and Technology Harbin Institute of Technology Harbin China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781577356332

Recent years have witnessed the growing popularity of hashing for efficient large-scale similarity search. It has been shown that the hashing quality could be boosted by hash function learning (HFL). In this paper, we study HFL in the context of multimodal data for cross-view similarity search. We present a novel multimodal HFL method, called Parametric Local Multimodal Hashing (PLMH), which learns a set of hash functions to locally adapt to the data structure of each modality. To balance locality and computational efficiency, the hashing projection matrix of each instance is parameterized, with guaranteed approximation error bound, as a linear combination of basis hashing projections of a small set of anchor points. A local optimal conjugate gradient algorithm is designed to learn the hash functions for each bit, and the overall hash codes are learned in a sequential manner to progressively minimize the bias. Experimental evaluations on cross-media retrieval tasks demonstrate that PLMH performs competitively against the state-of-the-art methods.

关键词： Hash functions

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2-D shape completion with shape priors

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Chinese Science Bulletin 2013年第27期58卷 3430-3436页

作者： GUO Ge JIANG TingTing WANG YiZhou GAO Wen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences National Engineering Laboratory for Video Technology Key Laboratory of Machine Perception School of EECS Peking University

We propose new techniques for 2-D shape/contour completion, which is one of the important research topics related to shape analysis and computer vision, e.g. the detection of incomplete objects due to occlusion and noises. The purpose of shape completion is to find the optimal curve segments that fill the missing contour parts, so as to acquire the best estimation of the original complete object shapes. Unlike the previous work using local smoothness or minimum curvature priors, we solve the problem under a Bayesian formulation taking advantage of global shape prior knowledge. With the priors, our methods are expert in recovering significant shape structures and dealing with large occlusion cases. There are two different priors adopted in this paper: (i) A generic prior model that prefers minimal global shape transformation (including non-rigid deformation and affine transformation with respect to a reference object shape) of the recovered complete shape; and (ii) a class-specific shape prior model learned from training examples of an object category, which prefers the reconstructed shape to follow the learned shape variation models of the category. Efficient contour completion algorithms are suggested corresponding to the two types of priors. Our experimental results demonstrate the advantage of the proposed shape completion approaches compared to the existing techniques, especially for objects with complex structure under severe occlusion.

关键词：形状结构计算机视觉贝叶斯公式电子相关最佳估计最小曲率先验知识仿射变换

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A novel graph-based compact representation of word alignment

A novel graph-based compact representation of word alignment

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Liu, Qun Tu, Zhaopeng Lin, Shouxun Centre for Next Generation Locolisation Dublin City University Ireland Key Lab. of Intelligent Info. Processing Institute of Computing Technology CAS China

ISBN: (纸本)9781937284510

In this paper, we propose a novel compact representation called weighted bipartite hypergraph to exploit the fertility model, which plays a critical role in word alignment. However, estimating the probabilities of rules extracted from hypergraphs is an NP-complete problem, which is computationally infeasible. Therefore, we propose a divide-and-conquer strategy by decomposing a hypergraph into a set of independent subhypergraphs. The experiments show that our approach outperforms both 1-best and n-best alignments. © 2013 Association for Computational Linguistics.

关键词： Computational complexity

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Failure Handling of E-Business Services in an Extensible Constraint Service Model

Failure Handling of E-Business Services in an Extensible Con...

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2013 IEEE 4th International Conference on Software Engineering and Service Science

作者： Ruoyu Chen Lejian Liao Zhi Fang Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications Beijing Key Laboratory of Intelligent Information TechnologySchool of Computer ScienceBeijing Institute of Technology

ISBN: (纸本)9781467349994

Failure handling of business transactions is essential in E-Business *** paper extends the service process specification proposed in a contract-centered constraint-based service modelling framework with failure handling *** failure in process model,generalized failure such as QoS violation can be modelled in *** semantics for failure handling in E-Business services as well as policies are discussed via a state transition system.A virtual machine is built for contracting and executing of services under this framework.

关键词： Services service Failure Handling of E-Business Services in an Extensible Constraint Service Model

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Attribute reduction based on approximation dependency degree

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Journal of Computers (Finland) 2013年第4期8卷 920-928页

作者： Li, Min Deng, ShaoBo Feng, Shengzhong Fan, Jianping NanChang Institute of Technology Nanchang Jiangxi 330099 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong 518055 China Key laboratory of intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

Attribute reduction is one of the core research content of Rough sets theory. Many existing algorithms mainly are aimed at the reduction of consistency decision table, and very little work has been done for attribute reduction aimed at inconsistency decision table. In fact, the methods finding Pawlak reduction from consistent decision table are not suitable for inconsistency decision table. In this paper, we introduce the approximation dependency reduction modal and present the Quick Attribution Reduction based on Approximation Dependency Degree (Quick-ARADD), which can retain the original boundary region and the original positive region unchanged, and keep the approximation accuracy unchanged for all decision equivalence classes (the partition of universe on decision attributes) of a decision table. Theoretical analysis and experimental results show that the Quick-ARADD algorithm is effective and feasible. © 2013 ACADEMY PUBLISHER.

关键词： Decision tables

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