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Selective Multi-Convolutional Region Feature Extraction based Iterative Discrimination CNN for Fine-Grained Vehicle Model recognition

Selective Multi-Convolutional Region Feature Extraction base...

引用

International conference on pattern recognition

作者： Yanling Tian Weitong Zhang Qieshi Zhang Gang Lu Xiaojun Wu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences China School of Computer Science Shaanxi Normal University Xi'an China

With the rapid rise of computer vision and driverless technology, vehicle model recognition plays a huge role in the common application and industry field. While fine-grained vehicle model recognition is often influenced by multi-level information, such as the image perspective, inter-feature similarity, vehicle details. Furthermore, pivotal regions extraction and fine-grained feature learning have become a vital obstacle to the fine-grained recognition of vehicle models. In this paper, we propose an iterative discrimination CNN (ID-CNN) based on selective multi-convolutional region (SMCR) feature extraction. the SMCR features, which consist of global and local SMCR features, are extracted from the original image with higher activation response value. As for ID-CNN, we use the global and local SMCR features iteratively to localize deep pivotal features and concatenate them together into a fully-connected fusion layer to predict the vehicle categories. We get better results and improve the accuracy to 91.8% on Stanford Cars-196 dataset and to 96.2% on CompCars dataset.

关键词： Feature extraction Convolution Computational modeling Image recognition pattern recognition Two dimensional displays

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L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space 30

L2-Net: Deep Learning of Discriminative Patch Descriptor in ...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tian, Yurun Fan, Bin Wu, Fuchao Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

the research focus of designing local patch descriptors has gradually shifted from handcrafted ones (e.g., SIFT) to learned ones. In this paper, we propose to learn high performance descriptor in Euclidean space via the Convolutional Neural Network (CNN). Our method is distinctive in four aspects: (i) We propose a progressive sampling strategy which enables the network to access billions of training samples in a few epochs. (ii) Derived from the basic concept of local patch matching problem, we emphasize the relative distance between descriptors. (iii) Extra supervision is imposed on the intermediate feature maps. (iv) Compactness of the descriptor is taken into account. the proposed network is named as L2-Net since the output descriptor can be matched in Euclidean space by L-2 distance. L2-Net achieves state-of-the-art performance on the Brown datasets [16], Oxford dataset [18] and the newly proposed Hpatches dataset [11]. the good generalization ability shown by experiments indicates that L2-Net can serve as a direct substitution of the existing handcrafted descriptors. the pre-trained L2-Net is publicly available.

关键词： L2-Net Deep Learning Discriminative Patch Descriptor in Euclidean Space Euclidean space Descriptors Subject Headings Dataset

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See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-based Person Re-identification 30

See the Forest for the Trees: Joint Spatial and Temporal Rec...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhou, Zhen Huang, Yan Wang, Wei Wang, Liang Tan, Tieniu Natl Lab Pattern Recognit NLPR Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Ctr Excellence Brain Sci & Intelligence Technol C Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China

ISBN: (纸本)9781538604571

Surveillance cameras have been widely used in different scenes. Accordingly, a demanding need is to recognize a person under different cameras, which is called person re-identification. this topic has gained increasing interests in computer vision recently. However, less attention has been paid to video-based approaches, compared with image-based ones. Two steps are usually involved in previous approaches, namely feature learning and metric learning. But most of the existing approaches only focus on either feature learning or metric learning. Meanwhile, many of them do not take full use of the temporal and spatial information. In this paper, we concentrate on video-based person re-identification and build an end-to-end deep neural network architecture to jointly learn features and metrics. the proposed method can automatically pick out the most discriminative frames in a given video by a temporal attention model. Moreover, it integrates the surrounding information at each location by a spatial recurrent model when measuring the similarity with another pedestrian video. that is, our method handles spatial and temporal information simultaneously in a unified manner. the carefully designed experiments on three public datasets show the effectiveness of each component of the proposed deep network, performing better in comparison with the state-of-the-art methods.

关键词： Measurement Image sequences Feature extraction computer architecture computer vision Recurrent neural networks

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Encoding the Models with Object-Aware Feature Basis: A New Analytical Tool for Graphic Applications

Encoding the Models with Object-Aware Feature Basis: A New A...

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International conference on Digital Home (ICDH)

作者： Nannan Li Haohao Li Jiangbei Hu Shengfa Wang Zhixun Su Zhongxuan Luo Department of Information Science and Technology Dalian Maritime University Dalian China School of Mathematical Science Dalian University of Technology Dalian China School of Software Technology Dalian University of Technology Dalian China

Feature space analysis is always the most central problem in all kinds of graphic applications, and the acquirement of different kinds of basis for feature space has never been stopped. In this paper, we propose a novel way to analyze the feature space by factorizing it into visually reasonable and physically meaningful basis and corresponding encoders (coefficients). Non-negative matrix factorization (NMF) has previously been shown to be powerful in information retrieval, computer vision and pattern recognition for its physically soundable and additive fashion. By transferring the factorization idea onto tasks of graphic applications, in this paper, we propose a framework for generating new feature basis and encoders for further analysis, which helps empower the downstream graphic applications, including analysis on one single model and joint analysis on a couple of models. Instead of factorizing the matrix composed of images or graphic elements/objects, we propose to apply sparse-and-constrained NMF (SAC-NMF) to the feature space that is more general and extendable. And by designing various feature descriptors, we get the base functions for the feature space to enable the analysis of one single model and co-analysis of a list of models. through the extensive experiments, our analytical framework has exhibited many attractive advantages such as being object-aware, robust, discriminative, extendable, etc.

关键词： Encoding Matrix decomposition Analytical models Shape Sparse matrices computer vision

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Effective Semi-supervised Learning Based on Local Correlation 18th

Effective Semi-supervised Learning Based on Local Correlatio...

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18th International conference on Computational Science (ICCS)

作者： Zhang, Xiao-Yu Wang, Shupeng Jin, Xin Zhu, Xiaobin Li, Binbin Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Natl Comp Network Emergency Response Tech Team Co Beijing Peoples R China Beijing Technol & Business Univ Beijing Peoples R China

ISBN: (纸本)9783319937137;9783319937120

Traditionally, the manipulation of unlabeled instances is solely based on prediction of the existing model, which is vulnerable to ill-posed training set, especially when the labeled instances are limited or imbalanced. To address this issue, this paper investigate the local correlation based on the entire data distribution, which is leveraged as informative guidance to ameliorate the negative influence of biased model. To formulate the self-expressive property between instances within a limited vicinity, we develop the sparse self-expressive representation learning method based on column-wise sparse matrix optimization. Optimization algorithm is presented via alternating iteration. then we further propose a novel framework, named semi-supervised learning based on local correlation, to effectively integrate the explicit prior knowledge and the implicit data distribution. In this way, the individual prediction from the learning model is refined by collective representation, and the pseudo-labeled instances are selected more effectively to augment the semi-supervised learning performance. Experimental results on multiple classification tasks indicate the effectiveness of the proposed algorithm.

关键词： Semi-supervised learning Local correlation Self-expressive representation Sparse matrix optimization Predictive confidence

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Adaptive Deep Metric Learning for Identity-Aware Facial Expression recognition 30

Adaptive Deep Metric Learning for Identity-Aware Facial Expr...

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30th IEEE/CVF conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Liu, Xiaofeng Kumar, B. V. K. Vijaya You, Jane Jia, Ping Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Beijing Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538607336

A key challenge of facial expression recognition (FER) is to develop effective representations to balance the complex distribution of intra- and inter- class variations. the latest deep convolutional networks proposed for FER are trained by penalizing the misclassification of images via the softmax loss. In this paper, we show that better FER performance can be achieved by combining the deep metric loss and softmax loss in a unified two fully connected layer branches framework via joint optimization. A generalized adaptive (N+M)-tuplet clusters loss function together with the identity-aware hard-negative mining and online positive mining scheme are proposed for identity-invariant FER. It reduces the computational burden of deep metric learning, and alleviates the difficulty of threshold validation and anchor selection. Extensive evaluations demonstrate that our method outperforms many state-of-art approaches on the posed as well as spontaneous facial expression databases.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Surveillance Video Parsing with Single Frame Supervision 30

Surveillance Video Parsing with Single Frame Supervision

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Si Wang, Changhu Qian, Ruihe Yu, Han Bao, Renda Sun, Yao Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Toutiao AI Lab Beijing Peoples R China

ISBN: (纸本)9781538604571

Surveillance video parsing, which segments the video frames into several labels, e.g., face, pants, left-leg, has wide applications [41, 8]. However, pixel-wisely annotating all frames is tedious and inefficient. In this paper, we develop a Single frame Video Parsing (SVP) method which requires only one labeled frame per video in training stage. To parse one particular frame, the video segment preceding the frame is jointly considered. SVP (i) roughly parses the frames within the video segment, (ii) estimates the optical flow between frames and (iii) fuses the rough parsing results warped by optical flow to produce the refined parsing result. the three components of SVP, namely frame parsing, optical flow estimation and temporal fusion are integrated in an end-to-end manner. Experimental results on two surveillance video datasets show the superiority of SVP over state-of-the-arts. the collected video parsing datasets can be downloaded via http:// liusi-group. com/ projects/SVP for the further studies.

关键词： Videotapes Parsing FRAMES Optic Flow SVP Frame Traffic estimation Single frame Leg

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Beyond triplet loss: a deep quadruplet network for person re-identification 30

Beyond triplet loss: a deep quadruplet network for person re...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Weihua Chen, Xiaotang Zhang, Jianguo Huang, Kaiqi CASIA CRIPAC&NLPR Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Dundee Comp Sch Sci & Engn Dundee Scotland CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9781538604571

Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.

关键词： Probes Training Testing Machine learning Feature extraction Loss measurement

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Identifying Transportation Modes Using Gradient Boosting Decision Tree 14th

Identifying Transportation Modes Using Gradient Boosting Dec...

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14th International conference on Intelligent Computing (ICIC)

作者： Fu, Xin Wang, Dong Zhang, Hengcai Univ Jinan Sch Resources & Environm Jinan 250022 Shandong Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China

ISBN: (纸本)9783319959320;9783319959337

Identifying the transportation modes could be applicable to many applications including personalized recommendation, transportation planning. the existing studies had not fully considered the impact of geographical information. In this paper, we propose a novel approach to detect transportation modes from massive trajectories using Gradient Boosting Decision Tree (GBDT), which adopted and estimated the impact of geographical information to achieve a better performance. In the experiments, we conduct the performance evaluation using the Geolife dataset which collected by 182 users over five years. the dataset contains 8347 trajectories with transportation mode such as driving, taking a bus, riding a bike and walking. 60% of trajectories are randomly chosen as training dataset, and then we tested on the remaining dataset. the experimental results showed that our proposed approach considering geographical information by using gradient boosting decision tree method achieve the precision of 84%, with the maximum increase of 6.83% to the traditional identifying transportation modes method. In addition, the geographical information contributed over 12% to improve the precision of recognition.

关键词： Trajectories Transportation mode GBDT pattern recognition Geographical features

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Latent Multi-view Subspace Clustering 30

Latent Multi-view Subspace Clustering

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Changqing Hu, Qinghua Fu, Huazhu Zhu, Pengfei Cao, Xiaochun Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore Singapore Chinese Acad Sci IIE State Key Lab Informat Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781538604571

In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and simultaneously performs data reconstruction based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict data themselves more comprehensively than each single view individually, accordingly makes subspace representation more accurate and robust as well. the proposed method is intuitive and can be optimized efficiently by using the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) algorithm. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

关键词： Linear programming Erbium Robustness Clustering methods Clustering algorithms Kernel Optimization

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