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A probabilistic model for automatic image annotation and retrieval

A probabilistic model for automatic image annotation and ret...

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IEEE 9th International Conference on Computer and information technology, CIT 2009

作者： Li, Zhixin Ma, Huifang Shi, Zhiping Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9780769538365

Automatic image annotation has become an important and challenging problem due to the existence of the semantic gap. In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to achieve the task. In order to model training data precisely, an image is firstly represented as a bag of visual words, then a probabilistic structure with two PLSA models is employed to capture semantic information from visual and textual modalities respectively. Furthermore, an adaptive asymmetric learning approach is proposed to fuse the aspects of these two models. For each image document, the distribution over aspects of different models is fused by multiplying different weights, which are determined by the entropy of the feature distribution. Consequently, the two models are linked with the same distribution over all aspects. This structure can predict semantic annotation well for an unseen image because it associates visual and textual modalities properly. We compare our approach with several previous approaches on a standard Corel dataset. The experimental results show that our approach performs more effectively and accurately. © 2009 IEEE.

关键词： Image retrieval

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Detecting unusual pattern with labeled data in two-stage

Detecting unusual pattern with labeled data in two-stage

引用

6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009

作者： Li, Jincheng He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780769537351

More analysis has been done to discover the meaningful unusual patterns which may mean fraud or anomaly. In this paper, a two-stage approach considering the labeled data is proposed to discover meaningful unusual observation, without the goal of classifying. We firstly apply Hyper Surface Classification (HSC) algorithm to gain a separating hyper surface which includes several pieces. Observation in the sparse piece is viewed as the unusual pattern. For other pieces with local density, we construct a weighted graph for it and search the Minimum Spanning Tree (MST), then detect further by cutting off several edges with the maximum weight. Combining the advantages of the two stages, a process of subdividing is proposed to consider the domain knowledge. Experimental results show that our approach can detect unusual pattern effectively together with other hidden valuable knowledge. © 2009 IEEE.

关键词： Trees (mathematics)

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Learning image semantics with latent aspect model

Learning image semantics with latent aspect model

引用

2009 IEEE International Conference on Multimedia and Expo, ICME 2009

作者： Zhixin, Li Xi, Liu Zhiping, Shi Zhongzhi, Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781424442911

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. ©2009 IEEE.

关键词： Image retrieval

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Filter object categories: Employing visual consistency and semi-supervised approach

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

引用

2009 IEEE International Conference on Multimedia and Expo, ICME 2009

作者： Xi, Liu Zhixin, Li Zhiping, Shi Zhongzhi, Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781424442911

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. ©2009 IEEE.

关键词： Bandpass filters

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Reducing SMT rule table with monolingual key phrase 47

Reducing SMT rule table with monolingual key phrase

引用

Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language processing of the AFNLP, ACL-IJCNLP 2009

作者： He, Zhongjun Meng, Yao Lü, Yajuan Yu, Hao Liu, Qun Fujitsu R and D Center CO. LTD Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781617382581

This paper presents an effective approach to discard most entries of the rule table for statistical machine translation. The rule table is filtered by monolingual key phrases, which are extracted from source text using a technique based on term extraction. Experiments show that 78% of the rule table is reduced without worsening translation performance. In most cases, our approach results in measurable improvements in BLEU score. © 2009 ACL and AFNLP.

关键词： Machine translation

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Constructing a facet-based and personalized just-in-time web information recommendation application in a multi-agent environment

Constructing a facet-based and personalized just-in-time web...

引用

IEEE 9th International Conference on Computer and information technology, CIT 2009

作者： Kun, Yang Zhongzhi, Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9780769538365

High precision is vital to the success of just-in-time information retrieval system. This paper attempts to improve it from two aspects: better understanding the user's current need and providing a highly relevant information source. For the former, an algorithm that can model a user's need in current context based on his behavior automatically is proposed, and for the latter, a mechanism is provided for the user to choose the information source he need by organizing all Web resources involved in a faceted way. All the algorithms and functions are encapsulated in several agents to constitute a multi-agent just-in-time Web information recommending application that help people write, and all these agent are constructed and managed by a multi-agent system middleware named "MAGE". © 2009 IEEE.

关键词： Middleware

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Automatic adaptation of annotation standards for dependency parsing - Using projected treebank as source corpus 11

Automatic adaptation of annotation standards for dependency ...

引用

11th International Conference on Parsing Technologies, IWPT 2009

作者： Jiang, Wenbin Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

We describe for dependency parsing an annotation adaptation strategy, which can automatically transfer the knowledge from a source corpus with a different annotation standard to the desired target parser, with the supervision by a target corpus annotated in the desired standard. Furthermore, instead of a hand-annotated one, a projected treebank derived from a bilingual corpus is used as the source corpus. This benefits the resource-scarce languages which haven't different hand-annotated treebanks. Experiments show that the target parser gains significant improvement over the baseline parser trained on the target corpus only, when the target corpus is smaller. © 2009 Association for Computational Linguistics

关键词： Forestry

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FILTER OBJECT CATEGORIES USING COBOOST WITH 1ST AND 2ND ORDER FEATURES

FILTER OBJECT CATEGORIES USING COBOOST WITH 1ST AND 2ND ORDE...

引用

International Conference on Image processing

作者： Xi Liu Zhiping Shi Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781424456536;9781424456543

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 lack of labeled object images. Our method deals with it by combining a co-training algorithm CoBoost [7] with two features - 1st and 2nd order features, which define bag of words representation and spatial relationship between local features respectively. We iteratively train two boosting classifiers based on the 1st and 2nd order features, during which each classifier provides labeled data for the other classifier. It is effective because the 1st and 2nd order features make up an independent and redundant feature split. We evaluate our method on Berg dataset and demonstrate the precision comparative to the state-of-the-art.

关键词： Filter object category Co-training CoBoost 1st order feature 2nd order feature Bag of words Classifiers Filters Category dimensional relationship PURSE

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The ICT Statistical Machine Translation Systems for the IWSLT 2009 6

The ICT Statistical Machine Translation Systems for the IWSL...

引用

6th International Workshop on Spoken Language Translation, IWSLT 2009

作者： Mi, Haitao Liu, Yang Xia, Tian Xiao, Xinyan Feng, Yang Xie, Jun Xiong, Hao Tu, Zhaopeng Zheng, Daqi Lu, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Haidian District P.O. Box 2704 Beijing100080 China

This paper describes the ICT Statistical Machine Translation systems that used in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2009. For this year's evaluation, we participated in the Challenge Task (Chinese-English and English-Chinese) and BTEC Task (Chinese-English). And we mainly focus on one new method to improve single system's translation quality. Specifically, we developed a sentence-similarity based development set selection technique. For each task, we finally submitted the single system who got the maximum BLEU scores on the selected development set. The four single translation systems are based on different techniques: a linguistically syntax-based system, two formally syntax-based systems and a phrase-based system. Typically, we didn't use any rescoring or system combination techniques in this year's evaluation. © IWSLT 2009. All Rights Reserved.

关键词： Computer aided language translation

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Image classification using structural sparse coding model

Image classification using structural sparse coding model

引用

5th International Conference on Natural Computation, ICNC 2009

作者： Li, Zhiqing Shi, Zhiping Li, Zhixin Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China College of Information Engineering Xiangtan University Xiangtan 411105 China

ISBN: (纸本)9780769537368

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. © 2009 IEEE.

关键词： Image classification

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