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2008 Conference on Human System Interactions (HSI 2008), vol.1

作者： Ruihong Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

This paper presents a candidate-evaluation model (CEM) which interactively elicits user preferences and assists decision makers in decision making in applications such as travel itinerary planning. The CEM contrasts with traditional decision analytic and planning frameworks in which a complete user model is elicited beforehand or is constructed by a human expert. We used the CEM model to implement an Itinerary Selection Assistant (ISA) system, which helps tourists identify satisfactory travel itineraries. The ISA starts with fuzzy user preferences and gradually approximate the optimal solution through carefully choosing candidate solutions to present to the user and inferring user's actual preferences by analyzing user evaluations over the candidates.

关键词： Augmented weighted Tchebycheff programming Fuzzy user preferences Multi-attribute decision making Travel itinerary User modeling

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COUNT PASSENGERS BASED ON HAAR-LIKE FEATURE IN ELEVATOR APPLICATION

COUNT PASSENGERS BASED ON HAAR-LIKE FEATURE IN ELEVATOR APPL...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： HONG LIU YUE-LIANG QIAN QUN LIU JIN-TAO LI Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080

Present elevator control use button sensors to determine when and where to dispatch an elevator car, which don't use the number of passengers. In this paper, we analyze images from camera to detect how many persons waiting for the elevator or in an elevator. A novel framework is proposed for optimized elevator schedule. Extended Haar-like features and Adaboost are used to train a head-shoulder classifier. Some images are selected from video according to elevator button callings to detect head-shoulder. To reduce false alarms a post process is added after detecting. Experimental results show the proposed method with post process has higher performance than existed methods. The information of passenger number can be send to elevator control system for effective schedule, which can reduce passengers waiting time and elevator's unnecessary stop, finally save energy and reduce maintain fee.

关键词： Elevator control system Haar-like features Adaboost Post process

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The Maximum Entropy based Rule Selection Model for Statistical Machine Translation (Invited Paper)

The Maximum Entropy based Rule Selection Model for Statistic...

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International Symposium on Universal Communication, ISUC

作者： Qun Liu Zhongjun He Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

This paper presents a novel rule selection model for statistical machine translation (SMT) that uses the maximum entropy approach to predict target-side for an ambiguous source-side. The maximum entropy based rule selection (MERS) model combines rich contextual information as features, thus can help SMT systems perform context-dependent rule selection. We incorporate the MERS model into two kinds of the state-of-the-art syntax-based SMT models: the hierarchical phrase-based model and the tree-to-string alignment template model. Experiments show that our approach achieves significant improvements over both the baseline systems.

关键词： Entropy Surface-mount technology Helium Decoding Predictive models Context modeling Power generation economics lab.ratories Machine intelligence information processing

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Focused Crawling with Heterogeneous Semantic information

Focused Crawling with Heterogeneous Semantic Information

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Rui Huang Fen Lin Zhongzhi Shi Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Focused crawlers selectively retrieve Web documents that are relevant to a predefined set of topics. To intelligently make predictions and decisions about relevant URLs and web pages, different topic models have been introduced to represent topic-specific knowledge. Yet it is difficult to support semantic interoperability among different models. Moreover, some manually specified additional semantic information, such as semantic markups and social annotations, could not be effectively used to improve crawling. This paper proposes to boost focused crawling with four kinds of semantic models and semantic information, including thesauruses, categories, ontologies, and folksonomies. A statistical semantic association model is proposed to integrate different semantic models, represent heterogeneous semantic information, and support semantic relevance computation. A focused crawling framework is developed which adopts both keyword based contents and different kinds of additional information for relevance prediction and ranking. Experiments show that the proposed model and framework effectively integrates heterogeneous semantic information for focused crawling.

关键词： Crawlers Web pages Uniform resource locators intelligent agent Computers Ontologies information processing Semantic Web information retrieval Predictive models

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Convergence property analysis for PSO based on cluster-degree

Convergence property analysis for PSO based on cluster-degre...

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2008 International Conference on Computational Intelligence and Security, CIS 2008

作者： Quansheng, Dou Zhijun, Yu Ping, Jiang Zhongzhi, Shi Yantai Dongfang Electronics Information Industry Group Co. Ltd. Yantai China School of Nursing Binzhou Medical University Yantai China School of Computer Science and Technology Shandong Institute of Business and Technology Yantai China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769535081

The concept of cluster-degree was put forward and distribute status of particle with different clusterdegree was studied. The reasonable parameters setting range based on cluster-degree was proposed. Under the direction of cluster-degree, many parameters which can get better searching results were found. So this paper is helpful for the choosing and adjustment of PSO parameters in practical application. © 2008 IEEE.

关键词： Convergence Computational intelligence Computer security information security information analysis Industrial electronics Computer industry Electronics industry Medical services Computer science

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A novel fuzzy likelihood measure algorithm

A novel fuzzy likelihood measure algorithm

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International Conference on Computer Science and Software Engineering, CSSE 2008

作者： Ding, Shifei Jin, Fengxiang School of Computer Science and Technology China University of Mining and Technology Xuzhou 221008 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China College of Geoinformation Science and Engineering Shandong University of Science and Technology Qingdao 266510 China

ISBN: (纸本)9780769533360

Similarity Measure(PSM) is a kind of measurement that measure the size of similarity between two patterns, it plays a key role in the analysis and research of pattern recognition, machine learning, clustering analysis. This article will firstly study the current PSM theory, point out its application range;secondly, discuss the axiomatic theory of fuzzy likelihood measure, give the frequently-used algorithms of fuzzy likelihood measure that based on axiomatic theory;finally, advance a new kind of fuzzy likelihood function, then establish the Fuzzy Likelihood Measure(FLM) between two fuzzy sets, so as to describe the similar degree between two fuzzy sets. FLM theory not only enriches and improves the PLS theory, and also provides new research methods for the theory research such as pattern recognition, machine learning, clustering analysis and so on. © 2008 IEEE.

关键词： Fuzzy sets

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Human reappearance detection based on on-line learning

Human reappearance detection based on on-line learning

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作者： Hu, Lei Wang, Yizhou Jiang, Shuqiang Huang, Qingming Gao, Wen Key Lab of Intelligent Information Processing Chinese Academy of Sciences Beijing 100080 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate University Chinese Academy of Sciences Beijing 100049 China Institute of Digital Media Peking University Beijing 100871 China Key Lab of Machine Perception Peking University MOE China

ISBN: (纸本)9781424421756

Many video surveillance applications require detecting human reappearances in a scene monitored by a camera or over a network of cameras. This is the human reappearance detection (HRD) problem. Studying this problem is important for analyzing a surveillance scenario at semantic level. In this paper, we propose a novel online learning frameworkfor solving HRD problem. Both generative model and discriminative model are employed in this framework and a voting scheme is presented to fuse the decisions ofboth models for determining whether a just entered person is one of those who have shown up, i.e. whether a reappearance happens. Both models will be updated based on mistake-driven online learning strategy. Our experimental results show that the adopted online learning framework not only improves the reappearance detection accuracy but also achieves high robustness in various surveillance scenes. © 2008 IEEE.

关键词： Cameras

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Hallucinating facial images and features

Hallucinating facial images and features

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作者： Li, Bo Chang, Hong Shan, Shiguang Chen, Xilin Gao, Wen Harbin Institute of Technology Harbin China Digital Media Research Center Institute of Computing Tech. CAS Beijing China Key Laboratory of Intelligent Information Processing CAS Beijing China Digital Media Institute Peking University Beijing China

ISBN: (纸本)9781424421756

In facial image analysis, image resolution is an important factor which has great influence on the performance of face recognition systems. As for low-resolution face recognition problem, traditional methods usually carry out super-resolution firstly before passing the super-resolved image to a face recognition system. In this paper, we propose a new method which predicts high-resolution images and the corresponding features simultaneously. More specifically, we propose "feature hallucination" to project facial images with low-resolution into an expected feature space. As a result, the proposed method does not require super-resolution as an explicit preprocessing step. In addition, we explore a constrained hallucination that considers the local consistency in the image grid. In our method, we use the index of local visual primitives [5] as features and a block-based histogram distance to measure the similarity for the face recognition. Experimental results on FERET face database verify that the proposed method can improve both visual quality and recognition rate for low-resolution facial images. © 2008 IEEE.

关键词： Image resolution

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Fast commercial detection based on audio retrieval

Fast commercial detection based on audio retrieval

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Dan Zhao Xiangdong Wang Yueliang Qian Qun Liu Shouxun Lin Institute of Computing Technology Key Laboratory of Intelligent Information Processing Beijing China Chinese Academy of Sciences Beijing China

Automatic detection of commercials in digital multimedia material is a challenging task with many applications. This paper presents a novel approach to fast commercial detection based on audio retrieval. It is based on the idea of segmenting energy envelope of audio into units, using only audio signal for matching on a commercial database. Fast searching and matching can be performed with high accuracy, by searching and by novel similarity function based on units. Experimental results show that 96.8% recall rate and 98.7% precision rate can be achieved under 0.125 real-time.

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Object-based image retrieval with attention analysis and spatial re-ranking

IFIP Advances in Information and Communication Technology

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IFIP Advances in information and Communication technology 2008年 288卷 118-128页

作者： Gao, Ke Lin, Shouxun Zhang, Yongdong Tang, Sheng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of the Chinese Academy of Sciences Beijing100080 China Key Laboratory of Intelligent Information Processing nstitute of Computing Technology Chinese Academy of Sciences Beijing100080 China

ISBN: (纸本)9780387876849

In this paper, a new method is proposed for object-based image retrieval. The user supplies a query object by selecting a region from a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large image database. The main outcomes of this research are as follows: (1) An novel object-based image retrieval framework that integrates effective pre-treatment and re-ranking is presented, (2) a new feature filtration method based on attention analysis is proposed for pre-treatment, (3) to further improve object retrieval precision, we add an efficient spatial configuration model to rerank the primary retrieval result using Bag of Word method. Experimental results demonstrate the effectiveness of our method. © 2008, Springer New York LLC . All rights rserved.

关键词： Query processing

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