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International Journal of Digital Content Technology and its Applications 2011年第7期5卷 335-349页

作者： Yang, Kun Huang, Rui Lin, Fen Shi, Zhongzhi Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China National Institute of Metrology 100013 Beijing China

This paper explores heterogeneous semantic data in Web 1.0, Semantic Web and Web 2.0 for topicspecific crawling and search. A statistical Semantic Association Model (SAM) is proposed to support semantic interoperability among four different models of thesauruses, categories, ontologies, and folksonomies. Based on this model, a focused crawling and semantic search framework is developed. In focused crawling of potentially related textual and semantic data, URLs are ordered before crawling and irrelevant Web pages are filtered out after crawling according to SAM-based semantic relevance ranking. In order that the retrieved results are more semantically related to the user queries, approaches of SAM-based semantic query expansion and meta-search result aggregation are designed. Experiments show that the proposed model and framework effectively integrates both keyword data and heterogeneous semantic data for topic-specific crawling and search.

关键词： Semantic Web

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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

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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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Constructing and analyzing a disease network based on proteins

Constructing and analyzing a disease network based on protei...

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作者： Ying Li Wencong Huang Guangxi Colleges and Universities Key Laboratory of Scientific Computing & Intelligent Information Processing Nanning Normal University School of Physical Education and Health Nanning Normal University

Protein is the specific executor of life activities,but there is no protein-based disease network and the current disease networks cannot show that a disease group share the same *** propose a method to construct a protein-based network by assigning disease pairs to different intervals according to their similarities and searching for disease groups in each *** methods are used to analyze the disease network,and the result indicates that :in the case where a disease belongs to only one disease group,most diseases have their own protein characteristics,but the common protein of them is not obvious;the more diseases a protein is related to,the more likely the protein becomes common protein;diseases grouping at protein level in this study are different from traditional disease classification;there is a certain relationship between disease symptoms and underlying proteins,but not one-to-one correspondence.

关键词： Constructing and analyzing a disease network based on proteins

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SEMANTICS CONSTRAINED DICTIONARY LEARNING FOR SIGNER-INDEPENDENT SIGN LANGUAGE RECOGNITION

SEMANTICS CONSTRAINED DICTIONARY LEARNING FOR SIGNER-INDEPEN...

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IEEE International Conference on Image processing

作者： Fang Yin Xiujuan Chai Yu Zhou Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Institute of Information Engineering Chinese Academy of Sciences

ISBN: (纸本)9781479983407

In this paper, a sparse coding based framework is proposed for sign language recognition (SLR), especially for the signer-independent case. To deal with the inter-signer variation, a dictionary capturing the common features among different signers is learnt by considering the semantic constraint. Thus for a given sign from an unknown signer, the sparse representation, which maintains more information of this specific sign class while neglecting the identity information as much as possible, can be generated. In our implementation, each sign is partitioned into a fixed number of fragments and the features fusing hand shape and moving trajectory are extracted from the fragments. The dictionary learnt from the training fragments can be taken as the basic subunits of signs and each fragment of sign video can be coded by these basis vectors. Finally, the recognition result is achieved through SVM with the concatenated sparse coding features of the fragments. The experiments and comparisons show that our method is more effective for the signer-independent recognition problem than other baseline methods. At the same time, it also performs well for the signer-dependent case.

关键词： Sparse coding Dictionary learning Sign language recognition Signer-independent Semantic constraint

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Boosting the Transferability of Video Adversarial Examples via Temporal Translation

arXiv

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arXiv 2021年

作者： Wei, Zhipeng Chen, Jingjing Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China

Although deep-learning based video recognition models have achieved remarkable success, they are vulnerable to adversarial examples that are generated by adding human-imperceptible perturbations on clean video samples. As indicated in recent studies, adversarial examples are transferable, which makes it feasible for black-box attacks in real-world applications. Nevertheless, most existing adversarial attack methods have poor transferability when attacking other video models and transfer-based attacks on video models are still unexplored. To this end, we propose to boost the transferability of video adversarial examples for black-box attacks on video recognition models. Through extensive analysis, we discover that different video recognition models rely on different discriminative temporal patterns, leading to the poor transferability of video adversarial examples. This motivates us to introduce a temporal translation attack method, which optimizes the adversarial perturbations over a set of temporal translated video clips. By generating adversarial examples over translated videos, the resulting adversarial examples are less sensitive to temporal patterns existed in the white-box model being attacked and thus can be better transferred. Extensive experiments on the Kinetics-400 dataset and the UCF-101 dataset demonstrate that our method can significantly boost the transferability of video adversarial examples. For transfer-based attack against video recognition models, it achieves a 61.56% average attack success rate on the Kinetics-400 and 48.60% on the UCF-101. Code is available at https://***/zhipeng-wei/TT. © 2021, CC BY.

关键词： Deep learning

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Generalized Unsupervised Manifold Alignment 14

Generalized Unsupervised Manifold Alignment

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Proceedings of the 28th International Conference on Neural information processing Systems - Volume 2

作者： Zhen Cui Hong Chang Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China and School of Computer Science and Technology Huaqiao University Xiamen China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

In this paper, we propose a Generalized Unsupervised Manifold Alignment (GU-MA) method to build the connections between different but correlated datasets without any known correspondences. Based on the assumption that datasets of the same theme usually have similar manifold structures, GUMA is formulated into an explicit integer optimization problem considering the structure matching and preserving criteria, as well as the feature comparability of the corresponding points in the mutual embedding space. The main benefits of this model include: (1) simultaneous discovery and alignment of manifold structures; (2) fully unsuper-vised matching without any pre-specified correspondences; (3) efficient iterative alignment without computations in all permutation cases. Experimental results on dataset matching and real-world applications demonstrate the effectiveness and the practicability of our manifold alignment method.

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Attacking Video Recognition Models with Bullet-Screen Comments

arXiv

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arXiv 2021年

作者： Chen, Kai Wei, Zhipeng Chen, Jingjing Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China

Recent research has demonstrated that Deep Neural Networks (DNNs) are vulnerable to adversarial patches which introduce perceptible but localized changes to the input. Nevertheless, existing approaches have focused on generating adversarial patches on images, their counterparts in videos have been less explored. Compared with images, attacking videos is much more challenging as it needs to consider not only spatial cues but also temporal cues. To close this gap, we introduce a novel adversarial attack in this paper, the bullet-screen comment (BSC) attack, which attacks video recognition models with BSCs. Specifically, adversarial BSCs are generated with a Reinforcement Learning (RL) framework, where the environment is set as the target model and the agent plays the role of selecting the position and transparency of each BSC. By continuously querying the target models and receiving feedback, the agent gradually adjusts its selection strategies in order to achieve a high fooling rate with non-overlapping BSCs. As BSCs can be regarded as a kind of meaningful patch, adding it to a clean video will not affect people's understanding of the video content, nor will arouse people's suspicion. We conduct extensive experiments to verify the effectiveness of the proposed method. On both UCF-101 and HMDB-51 datasets, our BSC attack method can achieve about 90% fooling rate when attacking three mainstream video recognition models, while only occluding © 2021, CC BY.

关键词： Deep neural networks

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Compressive sensing for wide-angle analysis

Compressive sensing for wide-angle analysis

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

作者： Du, Hong-Mei Chen, Ming-Sheng Wu, Xian-Liang Lu, Si-Long Mei, Yi-Cai Key Lab. of Intelligent Computing and Signal Processing Anhui University Ministry of Education Hefei 230029 China Department of Physics and Electronic Engineering Hefei Normal University Hefei 230601 China School of Information Science and Technology University of Science and Technology of China Hefei 230026 China

ISBN: (纸本)9781424469062

A new incident source with different angles was constructed for dealing with wide-angle scattering problems. Considering the impendence matrix in method of moments (MOM) is independent from incident angles, the equivalent relationship between induced current and the measured CS-current was build, while the CS-current can be computed directly under the new incident source. Finally, we can reconstruct the induce current by utilizing the theory of compressive sensing (CS). Compared with traditional MOM, the computational complexity can be greatly reduced. ©2010 IEEE.

关键词： Method of moments

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Anisotropic energy accumulation for stereoscopic image seam carving

Anisotropic energy accumulation for stereoscopic image seam ...

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International Conference on 3D Imaging (IC3D)

作者： Yuqing Lin Yuzhen Niu Shuai Zhang Junhao Chen College of Mathematics and Computer Science Fuzhou University Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing Fujian China

Seam carving is an image resizing method that aims at adapting the image to various display screens while reducing the distortion as much as possible. Severe visual distortion may be introduced by repeated removal or insertion of seams within a concentrated region of the image. To reduce such visual distortion, we propose a new AESSC method. First, we distribute the energy of each pixel on the seam to its adjacent 8-connected pixels when removing or inserting a seam. Second, since the information of each pixel is anisotropic, we use the Sobel operator to detect the direction that has the maximum edge information and continue the energy accumulation along this direction. Besides, we incorporate a 3D structure consistency constraint in the energy function and adopt a pixel visibility maintenance method. Experimental results show that the proposed method can effectively reduce visual distortion for stereo images while maintaining the geometric consistency.

关键词： Distortion Visualization Three-dimensional displays Stereo image processing Image edge detection Histograms Maintenance engineering

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