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Candidate generation for interactive Chinese speech recognition

Candidate generation for interactive Chinese speech recognit...

引用

Joint Conferences on Pervasive computing (JCPC)

作者： Xinhui Li Xiangdong Wang Yueliang Qian Shouxun Lin Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424452279;9781424452286

Despite progress in automatic speech recognition, user involvement and assistance are still needed to achieve performance similar to human in real-world tasks. Though many interactive speech recognition systems using candidate selection are developed for English and Japanese, similar systems for Chinese face particular difficulties due to characteristics of the Chinese language, especially at the candidate generation stage. In this paper, a method is proposed for candidate generation in interactive Chinese speech recognition, which lists all competitive candidates in forms of Chinese characters in stead of words. The main idea is to align time-overlapped links in Chinese word lattice into clusters based on their phonetic similarity, and then obtain candidates by splitting words attached to links into Chinese characters. Experimental results demonstrated that the candidates generated by the proposed method can lead to high performance in interactive Chinese speech recognition.

关键词： Speech recognition Lattices Character generation Error correction Natural languages Humans Automatic speech recognition Laboratories Competitive intelligence information processing

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Exploring Web Resources in Social Annotation Services in Hierarchy

Exploring Web Resources in Social Annotation Services in Hie...

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International Conference on Grid and Cooperative computing (GCC)

作者： Kun Yang Zhongzhi Shi Chinese Academy and Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

This paper considers the problem of the organizing way of Web resources in social annotation systems. Currently, the Web resources in social annotation systems contain multiple topics, organizing them into different semantic clusters will facilitate userspsila browsing and search greatly. This paper proposes a hierarchy clustering method to organize Web resources in hierarchy by making full use of the linkage relationship between the Web resources and their annotations. In this way, users can browse or search a Web resource collection in a Google mappsilas way by going deep into a collection with more details. Some experiments on data set from *** are performed to justify the effectiveness of our method and a prototype based on pictures in Flickr is developed to demonstrate our idea.

关键词： Laboratories information processing Computers Organizing Grid computing Clustering methods Couplings Prototypes Clustering algorithms YouTube

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Advertisement evaluation using visual saliency based on foveated image

Advertisement evaluation using visual saliency based on fove...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhiguo Ma Laiyun Qing Jun Miao Xilin Chen Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

This paper proposes a novel approach to advertisement evaluation using automatic salient regions. The salient regions are detected using a predicting model, in which the estimation are obtained by the space variant foveated image. The saliency is defined as the difference between the input image and its estimation. Then an advertisement is determined as attractive if the detected salient regions are overlapped with the interested regions of the advertisement. The experimental results on the advertisements data set are encouraging.

关键词： Computational modeling Humans Eyes Psychology Laboratories Computational intelligence information processing Computers Layout Machine learning

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Detection of Front-View Vehicle with Occlusions Using AdaBoost

Detection of Front-View Vehicle with Occlusions Using AdaBoo...

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International Conference on information Engineering and Computer Science (ICIECS)

作者： Chunpeng Wu Lijuan Duan Jun Miao Faming Fang Xuebin Wang College of Computer Sciences Beijing University of Technology Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424449934

In this paper, we propose a vehicle detection method based on AdaBoost. We focus on the detection of front-view car and bus with occlusions on highway. Samples with different occlusion situations are selected into the training set. By using basic and rotated Haar-like features extracted from the samples in the set, we train an AdaBoost-based cascade vehicle detector. The performance tests on static images and short time videos show that (1) our approach detects cars more effectively than buses (2) the real-time detection of our method on video proceeds at 30 frames per second.

关键词： Vehicle detection Feature extraction Prototypes Detectors Videos Educational institutions Road vehicles Face detection Boosting Laboratories

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Learning image semantics with latent aspect model

Learning image semantics with latent aspect model

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhixin Li Xi Liu Zhiping Shi Zhongzhi Shi Chinese Academy of Sciences Beijing Beijing CN The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to accomplish the tasks of semantic image annotation and retrieval. In order to model training images precisely, we employ two PLSA models to capture semantic information from visual and textual modalities respectively. Then an adaptive asymmetric learning approach is proposed to fuse aspects which are learned from both modalities. For each image document, the weight of each modality is determined by its contribution to the content of the image. Consequently, the two models are linked with the same distribution over aspects. This structure can predict semantic annotation for an unseen image because it associates visual and textual modalities properly. Finally, we compare our approach with several previous approaches on a standard Corel dataset. The experiment results show that our approach performs more effective and accurate.

关键词： Image retrieval Image databases Fuses Indexing Feature extraction Spatial databases Visual databases Hidden Markov models Laboratories information processing

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A Comparative Study of PCA, LDA and Kernel LDA for Image Classification

A Comparative Study of PCA, LDA and Kernel LDA for Image Cla...

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International Symposium on Ubiquitous Virtual Reality, ISUVR

作者： Fei Ye Zhiping Shi Zhongzhi Shi Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Although various discriminant analysis approaches have been used in content-based image retrieval (CBIR) application, there have been relatively few concerns with kernel-based methods. Furthermore, these CBIR applications still applied discriminant analysis to face images as face recognition did. In this paper we concerns images with general semantic concepts. We use our presented symmetrical invariant LBP (SILBP) texture descriptor to extract image visual features. We then explored effectiveness of principal component analysis (PCA), fisher linear discriminant analysis (LDA), and kernel LDA algorithms in providing optimal discrimination features. Following it, we present an LDA based framework to carry out kernel discrimiant analysis in our application. By taking advantage of the efficiency in nonlinear condition of kernel-based methods and the simplicity of LDA, the proposed approach can improve the retrieval precision of CBIR. The experimental results validate the effectiveness of the proposed approach.

关键词： Linear discriminant analysis Principal component analysis Kernel Image classification Image analysis information retrieval Image retrieval Content based retrieval Face recognition Feature extraction

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Adaptive action selection using utility-based reinforcement learning

Adaptive action selection using utility-based reinforcement ...

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IEEE International Conference on Granular computing (GRC)

作者： Kunrong Chen Fen Lin Qing Tan Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

A basic problem of intelligent systems is choosing adaptive action to perform in a non-stationary environment. Due to the combinatorial complexity of actions, agent cannot possibly consider every option available to it at every instant in time. It needs to find good policies that dictate optimum actions to perform in each situation. This paper proposes an algorithm, called UQ-learning, to better solve action selection problem by using reinforcement learning and utility function. Reinforcement learning can provide the information of environment and utility function is used to balance exploration-exploitation dilemma. We implement our method with maze navigation tasks in a non-stationary environment. The results of simulated experiments show that utility-based reinforcement learning approach is more effective and efficient compared with Q-learning and recency-based exploration.

关键词： Learning intelligent systems Adaptive systems information processing Computers Navigation Simulated annealing intelligent agent Management training Environmental management

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Emotion modeling in autonomic computing

Emotion modeling in autonomic computing

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IEEE International Conference on Cognitive Informatics

作者： Donglei Zhang Yu Pan Zhongzhi Shi Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Autonomic unit is the essential element and the most basic component of autonomic systems. Having the ability of handling emotions is considered to make autonomic unit more intelligent, more communicative and more social. In this article we try to build an emotion model for agents that act as autonomic units in autonomic systems and can self-manage its behavior to achieve the best performance. From four levels we define four models progressively and the top-level model is defined on the foundation of the three below, combining them as a unity. By given arousing axioms theoretically of several typical emotions, the model is proved to be effective for reasoning emotions of autonomic unit, and can be easily extended for dealing with other type of emotions. On this base an architecture of emotional autonomic unit is described. An example shows that emotion plays an important role in intelligent decision making, which proves the validity of the model and the special function in practice.

关键词： Cognition Cognitive science Humans Artificial intelligence Psychology Problem-solving information processing intelligent sensors Machine intelligence Computational modeling

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Filter object categories: employing visual consistency and semisupervised approach

Filter object categories: employing visual consistency and s...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xi Liu Zhixin Li Zhiping Shi Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

We describe a method for filtering object category from a large number of noisy images. This problem is particularly difficult due to the greater variation within object categories and only a few labeled object images available. Our method deals with it by using visual consistency and semi-supervised approach. The images of one category often share some visual consistency so that the most irrelevant images can be first removed. Among the left images, a voting method is used to obtain more object exemplars with the initial object exemplars manually selected by users. Finally with all the obtained exemplars and those unlabeled images, we create a semi-supervised classifier to rank all the images. We evaluate our method on Berg dataset and demonstrate the precision comparative to the state-of-the-art. Besides, we collect five more categories from Google images to show the effectiveness of the method.

关键词： Voting Support vector machines Support vector machine classification information filtering information filters Search engines Training data Laboratories information processing Computers

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Image Classification Using Structural Sparse Coding Model

Image Classification Using Structural Sparse Coding Model

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International Conference on Natural Computation (ICNC)

作者： Zhiqing Li Zhiping Shi Zhixin Li Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

ISBN: (纸本)9781424445424

Efficient coding hypothesis provides a quantitative relationship between environmental statistics and neural processing. In this paper, we put forward a novel sparse coding model based on structural similarity (SS_SC) for natural image feature extraction. The advantage for our model is to be able to preserve structural information from a scene, which human visual perception is highly adapted for. Using the proposed sparse coding model, the validity of image feature extraction is testified. Furthermore, inspired by Bayesian decision which is extensively used for classification, employing SS_SC we propose an algorithm for image classification. Compared with standard sparse coding (SC) model, the experimental results show that the quality of reconstructed images obtained by our method outperforms the SC method. Moreover, SS_SC model evidently enhances the classification accuracy.

关键词： Image classification Image coding Feature extraction Statistics Layout Humans Visual perception Testing Bayesian methods Classification algorithms

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