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The signal reconstruction of speech by KPCA 6

The signal reconstruction of speech by KPCA

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6th International Conference on Spoken Language processing, ICSLP 2000

作者： Yan, Hui Zhang, Xuegong Li, Yanda Shen, Liqin Zhu, Weibin Institute of Information Processing Department of Automation Key State Lab of Intelligent Technology and System Tsinghua University Beijing100084 China Speech Group IBM China Research Lab. No 26 6th Street Shangdi Haidian District Beijing100085 China

ISBN: (纸本)7801501144

A new method for speech signal reconstruction is proposed by performing a nonlinear Kernel Principal Component Analysis (KPCA). By the use of kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, and reconstruct vectors mapping from input space by those dominant principal components. As the reconstructed vectors is expressed in high dimensional feature space and they could not exist pre-image in input space. For finding pre-image, we use iteration method to approximate the pre-image. The experimental results using KPCA in data reconstruction and denoising in speech signal show that it had many potential advantages comparing with PCA.

关键词： Iterative methods

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Application of AWE to scattering analysis of the double-negative medium

Application of AWE to scattering analysis of the double-nega...

引用

2010 9th International Symposium on Antennas Propagation and EM Theory, ISAPE 2010

作者： Sun, Yu-Xin Chen, Ming-Sheng Wu, Xian-Liang Yang, Mei Wang, Hui Key Lab. of Intelligent Computing and Signal Processing Anhui University Ministry of Education Hefei China Department of Physics and Electronic Engineering Hefei Normal University Hefei China School of Information Science and Technology University of Science and Technology of China Hefei 230026 China

ISBN: (纸本)9781424469062

The PMCHWT equation of the double negative media (DNG) is obtained based on its constitutive relationship. And the surface currents and radar cross section (RCS) at a single frequency point is computed by Method of moments (MOM). The asymptotic waveform evaluation (AWE) technique for dispersive dielectric medium is deduced and apllied to electromagnetic scattering analysis of double-negative medium within a given frequency band. ©2010 IEEE.

关键词： Method of moments

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Exploring multi-loss function based attention framework for vehicle re-identification 10

Exploring multi-loss function based attention framework for ...

引用

10th International Conference on information Technology in Medicine and Education, ITME 2019

作者： Liu, Wei Zhang, Yi Luo, Zhiming Li, Shaozi East China Jiaotong University Jiangxi China Cognitive Science Department Xiamen University Fujian China Postdoc Center of Information and Communication Engineering Xiamen University China Key Lab. of Cogn. Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yi Chang China

ISBN: (纸本)9781728139173

Vehicle Re-identification(Re-ID) refers to the task of matching a specific vehicle across multiple non-overlapping cameras. Precise vehicle Re-ID suffers from two main challenges: (1) different vehicle identities with a similar appearance of the same type have a subtle inter-instance discrepancy, (2) one vehicle usually has large intra-instance differences due to viewpoint and illumination variations. To address these challenges, in this paper, we proposed an Attention-based Framework for Vehicle Re-ID named AFVR, which combines the benefits of both the cross-entropy loss function and the triplet loss function. Experimental results on two popular vehicle Re-ID benchmarks demonstrate the effectiveness of our proposed method, compared with the state of the arts. © 2019 IEEE.

关键词： Vehicles

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Leaf image recognition using Fourier transform based on ordered sequence

Leaf image recognition using Fourier transform based on orde...

引用

8th International Conference on intelligent computing Technology, ICIC 2012

作者： Yang, Li-Wei Wang, Xiao-Feng Department of Automation University of Science and Technology of China Hefei Anhui 230027 China Intelligent Computing Laboratory Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Key Lab. of Network and Intelligent Information Processing Hefei University Hefei Anhui 230601 China

ISBN: (纸本)9783642315879

There are a number of leaf recognition methods, but most of them are based on Euclidean space. In this paper, we will introduce a new description of feature for the leaf image recognition, which represents the leaf contour with the ordered sequence. For a leaf image, points on the contour represent the most important information of the leaf. Thus, by extracting serial points of the leaf contour, the unique corresponding ordered sequence can be obtained for a contour. Then, we can compute the amplitude-frequency feature by performing the Discrete Fourier transform on the ordered sequence. Since the low-frequency part of the Fourier transform represents the global information and the high-frequency part the local details, we can adopt the amplitude-frequency feature for leaf image recognition. Experimental results on the famous Swedish library and ICL library show that the proposed feature is effective for leaf image recognition. © 2012 Springer-Verlag.

关键词： Image recognition

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PKU at ImageCLEF 2008: Experiments with query extension techniques for text-based and content-based image retrieval

PKU at ImageCLEF 2008: Experiments with query extension tech...

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2008 Working Notes for CLEF Workshop, CLEF 2008 - Co-located with the 12th European Conference on Digital Libraries, ECDL 2008

作者： Zhou, Zhi Tian, Yonghong Li, Yuanning Liu, Ting Huang, Tiejun Gao, Wen Institute of Digital Media School of EE and CS Peking University Beijing100871 China Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing100190 China Graduate University Chinese Academy of Sciences Beijing100080 China

In this paper, we present our solutions for the WikipediaMM task at ImageCLEF 2008. The aim of this task is to investigate effective retrieval approaches in the context of a large-scale and heterogeneous collection of Wikipedia images that are searched by textual queries (and/or sample images and/or concepts) describing a user's information need. We first experimented with a text-based image retrieval approach with query extension, where the expansion terms are automatically selected from a knowledge base that is (semi-)automatically constructed from Wikipedia. We show how this open, constantly evolving encyclopedia can yield inexpensive knowledge structures that are specifically tailored to effectively enhance the semantics of queries. Encouragingly, the experimental results rank in the first place among all submitted runs. The second approach we experimented with is content-based image retrieval (CBIR), in which we first train 1-vs-all classifiers for all query concepts by using the training images obtained by Yahoo! search, and then treat the retrieval task as visual concept detection in the given Wikipedia image set. By comparison, this approach performs better than other submitted CBIR runs. Finally, we experimented with a cross-media image retrieval approach by combining and re-ranking text-based and content-based retrieval results. Despite the final experimental results were not formally submitted before the deadline, this approach performs remarkably better than the text-based retrieval or CBIR approaches.

关键词： Content based retrieval

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A new regression method based on SVM classification

A new regression method based on SVM classification

引用

2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Dong, Yi Cheng, Wei Li, Shengfeng Institute of Applied Mathematics Bengbu College Bengbu China School of Management Hefei Univ. of Technology Hefei China Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei China Anhui Vocational College of Electronics and Information Technology Bengbu China

ISBN: (纸本)9781612841816

SVM (Support Vector Machines) is a novel algorithm of machine learning which is based on SLT (Statistical Learning Theory). It can solve the problem characterized by nonlinear, high dimension, small sample and local minimizing perfectly. For non-linear problem, the forecasting technique of FCTR (First classification, then regression) was proposed, based on the classification approach of SVM and has carried on the simulation experiment. The experiment shows that the fitting value which obtains using the return to first would be more precise than directly. Using this method to food production forecast, its accuracy is superior to other production forecasting methods. © 2011 IEEE.

关键词： Support vector machines

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Automatic video genre categorization and event detection techniques on large-scale sports data 10

Automatic video genre categorization and event detection tec...

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Proceedings of the 2010 Conference of the Center for advanced Studies on Collab.rative research

作者： Zhang, Ning Duan, Ling-Yu Huang, Qingming Li, Lingfang Gao, Wen Guan, Ling Ryerson Multimedia Research Laboratory Ryerson University Toronto ON Canada Institute of Digital Media Peking University Beijing 100871 China Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate University of Chinese Academy of Sciences Beijing 100190 China

This paper presents an efficient and robust automatic process for large-scale sports video analysis. The proposed system firstly identifies the genre of the query video, and then accomplishes the interesting event detection task. The significance of this framework is its automatic characteristic in testing with minimum human involvement in training, as well as the scalab.lity and expansibility in dealing with a large-scale dataset. Domain-knowledge independent local features are extracted from an input video sequence and a histogram based distribution representation is created using the bag-of-visual-words (BoW) model. In genre categorization, k-nearest neighbor (k-NN) classifiers with various dissimilarity measures are assessed and evaluated analytically. For the event detection, a hidden conditional random field (HCRF) structured prediction model is utilized. Overall, this framework demonstrates the efficiency and accuracy in processing voluminous data from sports collection and achieves various tasks in video analysis. It also demonstrates a potential technology transformation from the "lab.ratory bench" to commercial applications. © 2010 Ning Zhang, Ling-Yu Duan, Qing-ming Huang, Lingfang Li, Wen Gao and Ling Guan.

关键词： Sports

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Benchmarking still-to-video face recognition via partial and local linear discriminant analysis on COX-S2V dataset

Benchmarking still-to-video face recognition via partial and...

引用

11th Asian Conference on Computer Vision, ACCV 2012

作者： Huang, Zhiwu Shan, Shiguang Zhang, Haihong Lao, Shihong Kuerban, Alifu Chen, Xilin Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China OMRON Social Solutions Co. Ltd. Kyoto Japan College of Information Science and Engineering Xinjiang University China

ISBN: (纸本)9783642374432

In this paper, we explore the real-world Still-to-Video (S2V) face recognition scenario, where only very few (single, in many cases) still images per person are enrolled into the gallery while it is usually possible to capture one or multiple video clips as probe. Typical application of S2V is mug-shot based watch list screening. Generally, in this scenario, the still image(s) were collected under controlled environment, thus of high quality and resolution, in frontal view, with normal lighting and neutral expression. On the contrary, the testing video frames are of low resolution and low quality, possibly with blur, and captured under poor lighting, in non-frontal view. We reveal that the S2V face recognition has been heavily overlooked in the past. Therefore, we provide a benchmarking in terms of both a large scale dataset and a new solution to the problem. Specifically, we collect (and release) a new dataset named COX-S2V, which contains 1,000 subjects, with each subject a high quality photo and four video clips captured simulating video surveillance scenario. Together with the database, a clear evaluation protocol is designed for benchmarking. In addition, in addressing this problem, we further propose a novel method named Partial and Local Linear Discriminant Analysis (PaLo-LDA). We then evaluated the method on COX-S2V and compared with several classic methods including LDA, LPP, ScSR. Evaluation results not only show the grand challenges of the COX-S2V, but also validate the effectiveness of the proposed PaLo-LDA method over the competitive methods. © 2013 Springer-Verlag.

关键词： Benchmarking

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Forest-based translation rule extraction

Forest-based translation rule extraction

引用

2008 Conference on Empirical Methods in Natural Language processing, EMNLP 2008, Co-located with AMTA 2008 and the International Workshop on Spoken Language Translation

作者： Mi, Haitao Huang, Liang Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Dept. of Computer and Information Science University of Pennsylvania Levine Hall 3330 Walnut St. Philadelphia PA 19104 United States

Translation rule extraction is a fundamental problem in machine translation, especially for linguistically syntax-based systems that need parse trees from either or both sides of the bitext. The current dominant practice only uses 1-best trees, which adversely affects the rule set quality due to parsing errors. So we propose a novel approach which extracts rules from a packed forest that compactly encodes exponentially many parses. Experiments show that this method improves translation quality by over 1 BLEU point on a state-of-the-art tree-to-string system, and is 0.5 points better than (and twice as fast as) extracting on 30- best parses. When combined with our previous work on forest-based decoding, it achieves a 2.5 BLEU points improvement over the baseline, and even outperforms the hierarchical system of Hiero by 0.7 points. © 2008 Association for Computational Linguistics.

关键词： Hierarchical systems

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A generic approach to classify sports video shots and its application in event detection 09

A generic approach to classify sports video shots and its ap...

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1st International Conference on Internet Multimedia computing and Service, ICIMCS 2009

作者： Li, Lingfang Duan, Ling-Yu Huang, Qingming Du, Jun Gao, Wen Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Institute of Digital Media Peking University Beijing 100871 China Graduate University of Chinese Academy of Sciences Beijing 100190 China NEC Research Labs. China Beijing 100084 China

ISBN: (纸本)9781605588407

Shot type is useful information for semantic sports video analysis. Most existing approaches utilize predefined rules and domain knowledge to derive shot types in sports video. Although these methods have achieved promising results in some specific games, it is hard to extend them from one sport to another. To address this problem, we propose a generic approach to classify shots in sports video. Our approach utilizes bag of visual words model to represent key frame for each shot based on Scale Invariant Feature Transform (SIFT) feature points;either Support Vector Machine (SVM) or Probabilistic Latent Semantic Analysis (PLSA) are then employed to classify key frame to determine shot type. As our approach relies little on domain knowledge, it can be more easily extended to different sports. We have evaluated our shot classification approach over five types of sports video and have achieved promising results. To show the usefulness and effectiveness of our shot classification, we apply the results of shot type to detect events in basketball video via a generative-discriminative model. In addition, we have observed that some common visual parts frequently appear across various shots in the same sport or even different but relevant sports. For instance, soccer and basketball are relevant sports in the sense of field-ball game. Motivated by this observation, we attempt to alleviate the problem of insufficient sports video data in some applications by sharing these visual parts across different but relevant kinds of sports. Copyright 2009 ACM.

关键词： Sports

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