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第十七届北京分析测试学术报告会暨展览会(BCEIA2017)——分论坛G

作者： Ming-Qi LIU Wen-Feng ZENG Wei-qian CAO Pan FANG Yang ZHANG Jian-Qiang WU Rui-Xiang SUN Si-Min He Peng-Yuan YANG Institutes of Biomedical Sciences and Department Chemistry Fudan University Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASand University of Chinese Academy of Sciences

We have developed a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry.A computational strategy called as p Glyco++has been decoded for this workflow,and ... 详细信息

关键词： PROTEOMIC IDENTIFICATION OF INTACT GLYCOPEPTIDES

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Plankton classification with deep convolutional neural networks

Plankton classification with deep convolutional neural netwo...

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2016 IEEE information technology, Networking, Electronic and Automation Control Conference, ITNEC 2016

作者： Py, Ouyang Hong, Hu Zhongzhi, Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467391924

Traditional methods for measuring and monitoring plankton populations are time consuming and can not scale to the granularity or scope necessary for large-scale studies. Improved approaches are needed. Manual analysis of the imagery captured by underwater camera system is infeasible. Automated image classification using machine learning tools is an alternative to the manual approach. In this paper, we present a deep neural network model for plankton classification which exploits translational and rotational symmetry. In this work, we propose two constrains in the design of deep convolutional neural network structure to guarantee the performance gain when going deep. Firstly, for each convolutional layer, its capacity of learning more complex patterns should be guaranteed;Secondly, the receptive field of the topmost layer should be no larger than the image region. We also developed a 'inception layer' like structure to deal with multi-size imagery input with convolutional neural network. The experimental result on Plankton Set 1.0 imagery data set show the feasibility and effectiveness of the proposed method. © 2016 IEEE.

关键词： Image classification

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Plankton Classification with Deep Convolutional Neural Networks

Plankton Classification with Deep Convolutional Neural Netwo...

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2016 IEEE information technology,Networking,Electronic and Automation Control Conference(ITNEC 2016)

作者： Ouyang py Hu Hong Shi zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781467391955

Traditional methods for measuring and monitoring plankton populations are time consuming and can not scale to the granularity or scope necessary for large-scale *** approaches are needed. Manual analysis of the imagery captured by underwater camera system is infeasible. Automated image classification using machine learning tools is an alternative to the manual approach. In this paper, we present a deep neural network model for plankton classification which exploits translational and rotational symmetry. In this work, we propose two constrains in the design of deep convolutional neural network structure to guarantee the performance gain when going ***, for each convolutional layer, its capacity of learning more complex patterns should be guaranteed;Secondly, the receptive field of the topmost layer should be no larger than the image region. We also developed a "inception layer" like structure to deal with multi-size imagery input with convolutional neural network. The experimental result on Plankton Set 1.0 imagery data set show the feasibility and effectiveness of the proposed method.

关键词： deep learning image classification convolutional neural network

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Heterogeneous convolutional neural networks for visual recognition 17th

Heterogeneous convolutional neural networks for visual recog...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Xiangyang Herranz, Luis Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319488950

Deep convolutional neural networks (CNNs) have shown impressive performance for image recognition when trained over large scale datasets such as ImageNet. CNNs can extract hierarchical features layer by layer starting from raw pixel values, and representations from the highest layers can be efficiently adapted to other visual recognition tasks. In this paper, we propose heterogeneous deep convolutional neural networks (HCNNs) to learn features from different CNN models. Features obtained from heterogeneous CNNs have different characteristics since each network has a different architecture with different depth and the design of receptive fields. HCNNs use a combination network (i.e. another multi-layer neural network) to learn higher level features combining those obtained from heterogeneous base neural networks. The combination network is also trained and thus can better integrate features obtained from heterogeneous base networks. To better understand the combination mechanism, we backpropagate the optimal output and evaluate how the network selects features from each model. The results show that the combination network can automatically leverage the different descriptive abilities of the original models, achieving comparable performance on many challenging benchmarks. © Springer International Publishing AG 2016.

关键词： Image recognition

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Where and what to eat: Simultaneous restaurant and dish recognition from food image 17th

Where and what to eat: Simultaneous restaurant and dish reco...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Wang, Huayang Min, Weiqing Li, Xiangyang Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319488899

This paper considers the problem of simultaneous restaurant and dish recognition from food images. Since the restaurants are known because of their some special dishes (e.g., the dish "hamburger" in the restaurant "KFC"), the dish semantics from the food image provides partial evidence for the restaurant identity. Therefore, instead of exploiting the binary correlation between food images and dish labels by existing work, we model food images, their dish names and restaurant information jointly, which is expected to enable novel applications, such as food image based restaurant visualization and recommendation. For solution, we propose a model, namely Partially Asymmetric Multi- Task Convolutional Neural Network (PAMT-CNN), which includes the dish pathway and the restaurant pathway to learn the dish semantics and the restaurant identity, respectively. Considering the dependence of the restaurant identity on the dish semantics, PAMT-CNN is capable of learning the restaurant’s identity under the guidance of the dish pathway using partially asymmetric shared network architecture. To evaluate our model, we construct one food image dataset with 24,690 food images, 100 classes of restaurants and 100 classes of dishes. The evaluation results on this dataset have validated the effectiveness of the proposed approach. © Springer International Publishing AG 2016.

关键词： Semantics

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Time matters: Multi-scale temporalization of social media popularity

arXiv

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arXiv 2017年

作者： Wu, Bo Cheng, Wen-Huang Zhang, Yongdong Mei, Tao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Research Center for Information Technology Innovation Academia Sinica Taiwan Microsoft Research China

The evolution of social media popularity exhibits rich temporality, i.e., popularities change over time at various levels of temporal granularity. This is influenced by temporal variations of public attentions or user activities. For example, popularity patterns of street snap on Flickr are observed to depict distinctive fashion styles at specific time scales, such as season-based periodic fluctuations for Trench Coat or one-off peak in days for Evening Dress. However, this fact is often overlooked by existing research of popularity modeling. We present the first study to incorporate multiple time-scale dynamics into predicting online popularity. We propose a novel computational framework in the paper, named Multi-scale Temporalization, for estimating popularity based on multi-scale decomposition and structural reconstruction in a tensor space of user, post, and time by joint low-rank constraints. By considering the noise caused by context inconsistency, we design a data rearrangement step based on context aggregation as preprocessing to enhance contextual relevance of neighboring data in the tensor space. As a result, our approach can leverage multiple levels of temporal characteristics and reduce the noise of data decomposition to improve modeling effectiveness. We evaluate our approach on two large-scale Flickr image datasets with over 1.8 million photos in total, for the task of popularity prediction. The results show that our approach significantly outperforms state-of-the-art popularity prediction techniques, with a relative improvement of 10.9%-47.5% in terms of prediction accuracy. Copyright © 2017, The Authors. All rights reserved.

关键词： Social networking (online)

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Live cell single molecule-guided Bayesian localization super resolution microscopy

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细胞研究（英文版） 2017年第5期27卷 713-716页

作者： Mingshu Zhang Wenting He Zhiyong Liu Fa Zhang Jennifer Lippincott-Schwartz Key Laboratory of RNA Biology Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Beijing Key Laboratory of Noncoding RNA Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia 20147 USA

Dear Editor,Many current super resolution (SR) microscopic techniques [1-6] have been successfully applied to image cellular dynamics in living cells,but their applications have remained technically *** cell stimulated emission depletion (STED)/reversible saturable optical linear fluorescence transitions (RESOLFT) microscopy and structured illumination microscopy (SIM)/nonlinear SIM require sophisticated expensive optical setups and specialized expertise for accurate optical *** cell photo-activated localization microscopy (PALM)/stochastic optical reconstruction microscopy (STORM)use less complicated setup;however,a scientific complementary metal-oxide-semiconductor (sCMOS) camera,whose pixel-dependent noise must be characterized and calibrated before use [7],is required for extremely high acquisition speed over tens of thousands of ***,wide field-based SR microscopies have been developed to improve temporal resolution using much fewer time-lapse images (hundreds to thousands) than PALM/STORM [8,9].One of them,Bayesian analysis of the blinking and bleaching (3B) [8],offers enormous potential to resolve ultrastructure and fast cellular dynamics in living cells beyond the diffraction *** its potential,3B analysis is impractical when imaging the nanoscale dynamics in large fields of view over long time periods,as the calculation is extremely time-consuming [8] and/or the analysis consumes large amounts of web resources [10].Another major problem of 3B imaging is the artificial thinning and thickening of structures both in simulated image data and in experimental data [8,10].

关键词： @@

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Block-Based Projection Matrix Design for Compressed Sensing

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Chinese Journal of Electronics 2016年第3期25卷 551-555页

作者： LI Zhetao XIE Jingxiong ZHU Gengming PENG Xin XIE Yanrong CHOI Youngjune The College of Information Engineering Xiangtan University School of Computer National University of Defense Technology Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University School of Computer Science and Engineering Hunan University of Science and Technology College of Information and Communication Engineering Hunan Institute of Science and Technology Department of Information and Computer Engineering Ajou University

The objective of optimizing a projection matrix is to decrease the mutual coherence between a projection matrix and a basis matrix. In this paper, a novel block-based method is proposed to design a projection matrix in compressed sensing. Here, the projection matrix is divided into two blocks. The relationship between the two blocks was obtained by reasoning and proving. Theoretical analysis demonstrates that the mutual coherence between the whole projection matrix and the whole basis matrix keeps as good as the mutual coherence between the block matrix and blocked basis matrix. Experimental results show that the proposed method obtains better performance compared to existing methods.

关键词： Compressed sensing Projection matrix Block optimization Mutual coherence

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4D ISIP: 4D implicit surface interest point detection

arXiv

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arXiv 2017年

作者： Li, Shirui Yilmaz, Alper Xiao, Changlin Li, Hua Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Ohio State University OH United States

In this paper, we proposed a new method to detect 4D spatiotemporal interest point called 4D-ISIP(4 dimension implicit surface interest point). We implicitly represent the 3D scene by 3D volume which has a truncated signed distance function (TSDF) in every voxel. The TSDF represents the distance between the spatial point and object surface which is a kind of implicit surface representation. The basic idea of 4D-ISIP detection is to detect the points whose local neighborhood has significant variations along both spatial and temporal dimensions. In order to test our 4D-ISIP detection, we built a system to acquire 3D human motion dataset using only one Kinect. Experimental results show that our method can detect 4D-ISIP for different human actions. Copyright © 2017, The Authors. All rights reserved.

关键词： Image reconstruction

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Reflection invariant and symmetry detection

arXiv

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arXiv 2017年

作者： Li, Erbo Li, Hua EON Reality Inc IrvineCA92618 United States Key lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Symmetry detection and discrimination are of fundamental meaning in science, technology, and engineering. This paper introduces reflection invariants and defines the directional moments(DMs) to detect symmetry for shape analysis and object recognition. And it demonstrates that detection of reflection symmetry can be done in a simple way by solving a trigonometric system derived from the DMs, and discrimination of reflection symmetry can be achieved by application of the reflection invariants in 2D and 3D. Rotation symmetry can also be determined based on that. Also, if none of reflection invariants is equal to zero, then there is no symmetry. And the experiments in 2D and 3D show that all the reflection lines or planes can be deterministically found using DMs up to order six. This result can be used to simplify the efforts of symmetry detection in research areas,such as protein structure, model retrieval, reverse engineering, and machine vision etc. Copyright © 2017, The Authors. All rights reserved.

关键词： Object recognition

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