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IEEE Conference on Computer Vision and Pattern Recognition

作者： Haomiao Liu Ruiping Wang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (纸本)9781467388528

In this paper, we present a new hashing method to learn compact binary codes for highly efficient image retrieval on large-scale datasets. While the complex image appearance variations still pose a great challenge to reliable retrieval, in light of the recent progress of Convolutional Neural Networks (CNNs) in learning robust image representation on various vision tasks, this paper proposes a novel Deep Supervised Hashing (DSH) method to learn compact similarity-preserving binary code for the huge body of image data. Specifically, we devise a CNN architecture that takes pairs of images (similar/dissimilar) as training inputs and encourages the output of each image to approximate discrete values (e.g. +1/-1). To this end, a loss function is elaborately designed to maximize the discriminability of the output space by encoding the supervised information from the input image pairs, and simultaneously imposing regularization on the real-valued outputs to approximate the desired discrete values. For image retrieval, new-coming query images can be easily encoded by propagating through the network and then quantizing the network outputs to binary codes representation. Extensive experiments on two large scale datasets CIFAR-10 and NUS-WIDE show the promising performance of our method compared with the state-of-the-arts.

关键词： image retrieval binary code hashing hashing method images discrete value image representation LOSS FUNCTION image data Propagating Imagery (Psychotherapy)

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Category aggregation among region proposals for object detection 17th

Category aggregation among region proposals for object detec...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Linghui Tang, Sheng Zhou, Jianshe Wang, Bin Tian, Qi Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing100190 China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University Beijing100048 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249-1604 United States

ISBN: (纸本)9783319488950

Recently, an overwhelming majority of object detection methods have focused on how to reduce the number of region proposals while keeping high object recall without consideration of category information. It may lead to a lot of false positives due to the interferences between categories especially when the number of categories is very large. To eliminate such interferences, we propose a novel category aggregation approach based upon our observation that more frequently detected categories around an object have the higher probabilities to be present in an image. After further exploiting the co-occurrence relationship between categories, we can determine the most possible categories for an image in advance. Thus, many false positives can be greatly filtered out before subsequent classification process. Our extensive experiments on the well-known ILSVRC 2015 detection dataset show that our approach can achieve 49.0% of mAP in the validation dataset and 45.36% of mAP in the test dataset ranked 5th in the ILSVRC 2015 detection task. © Springer International Publishing AG 2016.

关键词： Object detection

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RGB-D Hand-Held Object Recognition Based on Heterogeneous Feature Fusion

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Journal of Computer Science & technology 2015年第2期30卷 340-352页

作者：吕雄蒋树强 Luis Herranz 王双 Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Object recognition has many applications in human-machine interaction and multimedia retrieval. However, due to large intra-class variability and inter-class similarity, accurate recognition relying only on RGB data is still a big challenge. Recently, with the emergence of inexpensive RGB-D devices, this challenge can be better addressed by leveraging additional depth information. A very special yet important case of object recognition is hand-held object recognition, as manipulating objects with hands is common and intuitive in human-human and human-machine interactions. In this paper, we study this problem and introduce an effective framework to address it. This framework first detects and segments the hand-held object by exploiting skeleton information combined with depth information. In the object recognition stage, this work exploits heterogeneous features extracted from different modalities and fuses them to improve the recognition accuracy. In particular, we incorporate handcrafted and deep learned features and study several multi-step fusion variants. Experimental evaluations validate the effectiveness of the proposed method.

关键词： RGB-D hand-held object recognition heterogeneous features fusion

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Large-scale person re-identification as retrieval

Large-scale person re-identification as retrieval

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hantao Yao Shiliang Zhang Dongming Zhang Yongdong Zhang Jintao Li Yu Wang Qi Tian University of Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Texas at San Antonio San Antonio USA

ISBN: (纸本)9781509060689

This paper targets to bring together the research efforts on two fields that are growing actively in the past few years: multicamera person Re-Identification (ReID) and large-scale image retrieval. We demonstrate that the essentials of image retrieval and person ReID are the same, i.e., measuring the similarity between images. However, person ReID requires more discriminative and robust features to identify the subtle differences of different persons and overcome the large variance among images of the same person. Specifically, we propose a coarse-to-fine (C2F) framework and a Convolutional Neural Network structure named as Conv-Net to tackle the large-scale person ReID as an image retrieval task. Given a query person image, the C2F firstly employ Conv-Net to extract a compact descriptor and perform the coarse-level search. A robust descriptor conveying more spatial cues is hence extracted to perform the fine-level search. Extensive experimental results show that the proposed method outperforms existing methods on two public datasets. Further, the evaluation on a large-scale Person-520K dataset demonstrates that our work is significantly more efficient than existing works, e.g., only needs 180ms to identify a query person from 520K images.

关键词： Training Feature extraction Cameras Robustness Image retrieval Linear programming

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Tri-level combination for image representation 17th

Tri-level combination for image representation

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Ruiying Zhang, Chunjie Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319488899

The context of objects can provide auxiliary discrimination beyond objects. However, this effective information has not been fully explored. In this paper, we propose Tri-level Combination for Image Representation (TriCoIR) to solve the problem at three different levels: object intrinsic, strongly-related context and weakly-related context. Object intrinsic excludes external disturbances and more focuses on the objects themselves. Strongly-related context is cropped from the input image with a more loose bound to contain surrounding context. Weaklyrelated one is recovered from the image other than object for global context. First, strongly and weakly-related context are constructed from input images. Second, we make cascade transformations for more intrinsical object information, which depends on the consistency between generated global context and input images in the regions other than object. Finally, a joint representation is acquired based on these three level features. The experiments on two benchmark datasets prove the effectiveness of TriCoIR. © Springer International Publishing AG 2016.

关键词： Image representation

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UNSUPERVISED PERSON RE-IDENTIFICATION WITH LOCALITY-CONSTRAINED EARTH MOVER'S DISTANCE

UNSUPERVISED PERSON RE-IDENTIFICATION WITH LOCALITY-CONSTRAI...

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IEEE International Conference on Image processing

作者： Dan Wang Canxiang Yan Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Institute of Deep Learning Baidu Inc.

ISBN: (纸本)9781467399623

The difficult acquisition of labeled data and the misalignment of local matching are major obstacles to apply person re-identification in real scenarios. To alleviate these problems, we propose an unsupervised method, called locality-constrained Earth Mover's Distance (LC-EMD), to learn the optimal measure between image pairs. Specifically, Gaussian mixture models (GMMs) are learned as signatures. By imposing locality constraints, LC-EMD can naturally achieve partial matching between Gaussian components. Moreover, LC-EMD has the analytical solution which can be efficiently computed. Experiments on two public datasets demonstrate LC-EMD is robust to misalignment and performs better than other unsupervised methods.

关键词： Identification of persons Distance learning Gaussian mixture model Distance learning Misalignment Mixture models EARTHMOVER

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Consensus algorithms for biased labeling in crowdsourcing

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information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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The sound and complete R-calculus for revising propositional theories

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Science China(information Sciences) 2015年第9期58卷 5-16页

作者： LI Wei SUI YueFei SUN MeiYing State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

The AGM postulates are for the belief revision(revision by a single belief), and the DP postulates for the iterated revision(revision by a finite sequence of beliefs). Li gave an R-calculus for R-configurations?|Γ,where ? is a set of atomic formulas or the negations of atomic formulas, and Γ is a finite set of formulas. In this paper, two deduction systems for the revision of a theory by another theory are given such that the systems are sound and complete, that is, if ?|Γ■Γ′ is provable then Γ′■ ? is consistent and Γ′- ? is a maximal subset of Γ such that(Γ′- ?) ∪ ? is consistent; and for any finite theories ? and Γ, there is a finite theoryΓ′ such that Γ′- ? is a maximal subset of Γ such that(Γ′- ?) ∪ ? is consistent, and ?|Γ ? Γ′ is ***, if?|Γ■Γ′ is provable then Γ′ satisfies the AGM and the DP postulates.

关键词： belief revision R-calculus minimal change the AGM postulates the DP postulates

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A New Acquisition Method Based on Differential Correlation

A New Acquisition Method Based on Differential Correlation

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2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016)

作者： Sujuan Shang Yanjun Hu Jufeng Luo Yingguan Wang Key laboratory of Intelligent Computing & Signal Processing (Anhui University) Ministry of Education Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences

Acquisition is a key technology in DSSS *** differential correlation is usually employed to eliminate the effect of frequency ***,as the length of pseudo-code grows and the decrease of the SNR,the traditional differential acquisition algorithms will result in great loss of *** paper presents an improved differential acquisition method,named as M-orders Auto-Correlation based on Differential correlation（MAC-DF）.In the proposed method,the input signal is multiplied by the complex conjugate of the pseudo-code to eliminate its *** that,the product is applied to the M-orders Auto-Correlation to compensate the SNR loss caused by the differential *** means of mathematical model to analyze its acquisition *** compare MAC-DF acquisition algorithm with the differential coherent and non-coherent acquisition algorithms through *** simulation results indicate that this algorithm is approximately 5-6dB superior to the traditional differential acquisition algorithms in improving acquisition sensitivity and more adaptive to work under low SNR.

关键词： DSSS Pseudo-code acquisition low SNR differential correlation acquisition algorithm

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Color Image Segmentation Based on Modified Kuramoto Model

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Procedia Computer Science 2016年 88卷 245-258页

作者： Xiaojie Liu Yuanhua Qiao Xianghui Chen Jun Miao Lijuan Duan College of Applied Sciences Beijing University of Technology Beijing 100124 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China College of Computer Science and Technology Beijing University of Technology Beijing 100124 China

A new approach for color image segmentation is proposed based on Kuramoto model in this paper. Firstly, the classic Kuramoto model which describes a global coupled oscillator network is changed to be one that is locally coupled to simulate the neuron activity in visual cortex and to describe the influence for phase changing by external stimuli. Secondly, a rebuilt method of coupled neuron activities is proposed by introducing and computing instantaneous frequency. Three oscillating curves corresponding to the pixel values of R, G, B for color image are formed by the coupled network and are added up to produce the superposition of oscillation. Finally, color images are segmented according to the synchronization of the oscillating superposition by extracting and checking the frequency of the oscillating curves. The performance is compared with that from other representative segmentation approaches.

关键词： Kuramoto model Neural Network Color image segmentation

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