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Machine Learning and Data Mining in pattern recognition 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Petra Perner

ISBN: (数字)9783642231995

ISBN: (纸本)9783642231988

this book constitutes the refereed proceedings of the 7th International conference on Machine Learning and Data Mining in pattern recognition, MLDM 2011, held in New York, NY, USA. the 44 revised full papers presented were carefully reviewed and selected from 170 submissions. the papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining; and machine learning and image mining.

关键词： Artificial Intelligence Mathematical Logic and Formal Languages Database Management Data Mining and Knowledge Discovery pattern recognition Image Processing and computer vision

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OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval

OMG: Observe Multiple Granularities for Natural Language-Bas...

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

作者： Du, Yunhao Zhang, Binyu Ruan, Xiangning Su, Fei Zhao, Zhicheng Chen, Hong Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Retrieving tracked-vehicles by natural language descriptions plays a critical role in smart city construction. It aims to find the best match for the given texts from a set of tracked vehicles in surveillance videos. Existing works generally solve it by a dual-stream framework, which consists of a text encoder, a visual encoder and a cross-modal loss function. Although some progress has been made, they failed to fully exploit the information at various levels of granularity. To tackle this issue, we propose a novel framework for the natural language-based vehicle retrieval task, OMG, which Observes Multiple Granularities with respect to visual representation, textual representation and objective functions. For the visual representation, target features, context features and motion features are encoded separately. For the textual representation, one global embedding, three local embeddings and a color-type prompt embedding are extracted to represent various granularities of semantic features. Finally, the overall framework is optimized by a cross-modal multi-granularity contrastive loss function. Experiments demonstrate the effectiveness of our method. Our OMG significantly outperforms all previous methods and ranks the 9th on the 6th AI City Challenge Track2.

关键词： Representation learning Visualization Smart cities Surveillance Semantics Feature extraction pattern recognition

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Towards Accurate Scene Text recognition with Semantic Reasoning Networks

Towards Accurate Scene Text Recognition with Semantic Reason...

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

作者： Yu, Deli Li, Xuan Zhang, Chengquan Liu, Tao Han, Junyu Liu, Jingtuo Ding, Errui Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Baidu Inc Beijing Peoples R China

ISBN: (纸本)9781728171685

Scene text image contains two levels of contents: visual texture and semantic information. Although the previous scene text recognition methods have made great progress over the past few years, the research on mining semantic information to assist text recognition attracts less attention, only RNN-like structures are explored to implicitly model semantic information. However, we observe that RNN based methods have some obvious shortcomings, such as time-dependent decoding manner and one-way serial transmission of semantic context, which greatly limit the help of semantic information and the computation efficiency. To mitigate these limitations, we propose a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition, where a global semantic reasoning module (GSRM) is introduced to capture global semantic context through multi-way parallel transmission. the state-of-the-art results on 7 public benchmarks, including regular text, irregular text and non-Latin long text, verify the effectiveness and robustness of the proposed method. In addition, the speed of SRN has significant advantages over the RNN based methods, demonstrating its value in practical use.

关键词： Semantics

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Radial Lens Distortion Correction by Adding a Weight Layer with Inverted Foveal Models to Convolutional Neural Networks 24

Radial Lens Distortion Correction by Adding a Weight Layer w...

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24th International conference on pattern recognition (ICPR)

作者： Shi, Yongjie Zhang, Danfeng Wen, Jingsi Tong, Xin Ying, Xianghua Zha, Hongbin Peking Univ Sch Elect Engn & Comp Sci Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (纸本)9781538637883

Radial lens distortion often exists in images taken by commercial cameras, which does not satisfy the assumption of pinhole camera model. Eliminating the radial lens distortion of an image is necessary as a preprocessing step for many vision applications. Some paper has employed Convolutional Neural Networks (CNNs), to achieve radial distortion correction. they generated images with a large number of images of high variation of radial distortion, which can be well exploited by deep CNN with a high learning capacity, and reach the state-of-the-art results. In this paper, we claim that a weight layer with inverted foveal models can be added to these existing CNNs methods for radial distortion correction. In the widely used very deep Resnet-18 model, our method achieves about 20 percent decrease in the loss function with faster convergence compared to the previous methods.

关键词： Nonlinear distortion Lenses Computational modeling pattern recognition Cameras computer vision

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Cumulative Attribute Space for Age and Crowd Density Estimation

Cumulative Attribute Space for Age and Crowd Density Estimat...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Chen, Ke Gong, Shaogang Xiang, Tao Loy, Chen Change Univ London London E1 4NS England Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

A number of computer vision problems such as human age estimation, crowd density estimation and body/face pose (view angle) estimation can be formulated as a regression problem by learning a mapping function between a high dimensional vector-formed feature input and a scalar-valued output. Such a learning problem is made difficult due to sparse and imbalanced training data and large feature variations caused by both uncertain viewing conditions and intrinsic ambiguities between observable visual features and the scalar values to be estimated. Encouraged by the recent success in using attributes for solving classification problems with sparse training data, this paper introduces a novel cumulative attribute concept for learning a regression model when only sparse and imbalanced data are available. More precisely, low-level visual features extracted from sparse and imbalanced image samples are mapped onto a cumulative attribute space where each dimension has clearly defined semantic interpretation (a label) that captures how the scalar output value (e. g. age, people count) changes continuously and cumulatively. Extensive experiments show that our cumulative attribute framework gains notable advantage on accuracy for both age estimation and crowd counting when compared against conventional regression models, especially when the labelled training data is sparse with imbalanced sampling.

关键词： computer vision feature extraction image classification learning (artificial intelligence) pose estimation regression analysis

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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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Discriminative Covariance Oriented Representation Learning for Face recognition with Image Sets 30

Discriminative Covariance Oriented Representation Learning f...

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

作者： Wang, Wen Wang, Ruiping Shan, Shiguang Chen, Xilin Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Cooperat Medianet Innovat Ctr Beijing Peoples R China

ISBN: (纸本)9781538604571

For face recognition with image sets, while most existing works mainly focus on building robust set models with hand-crafted feature, it remains a research gap to learn better image representations which can closely match the subsequent image set modeling and classification. Taking sample covariance matrix as set model in the light of its recent promising success, we present a Discriminative Covariance oriented Representation Learning (DCRL) framework to bridge the above gap. the framework constructs a feature learning network (e.g. a CNN) to project the face images into a target representation space, and the network is trained towards the goal that the set covariance matrix calculated in the target space has maximum discriminative ability. To encode the discriminative ability of set covariance matrices, we elaborately design two different loss functions, which respectively lead to two different representation learning schemes, i.e., the Graph Embedding scheme and the Softmax Regression scheme. Both schemes optimize the whole network containing both image representation mapping and set model classification in a joint learning manner. the proposed method is extensively validated on three challenging and large scale databases for the task of face recognition with image sets, i.e., YouTube Celebrities, YouTube Face DB and Point-and-Shoot Challenge.

关键词： Facial recognition images Image image representation variance covariance matrix modeling light Imagery (Psychotherapy) image set LOSS FUNCTION Covariance YouTube SETTING Scheme Set (Psychology) Covariance matrix

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thinning based multipurpose camouflage pattern design

Thinning based multipurpose camouflage pattern design

引用

2011 7th Iranian conference on Machine vision and Image Processing, MVIP 2011

作者： Hejrandoost, Arash Abdi Safabakhsh, Reza Amirkabir University Tehran Iran

ISBN: (纸本)9781457715358

Traditional generations of camouflage pattern were an artistic and manually work. Most of current computer aided camouflage pattern designs focused on a single background to produce the pattern for. In our proposed method, a multi background camouflage pattern is designed using Fuzzy C-Means (FCM) clustering method followed by a object thinning algorithm to combine clustered backgrounds with each other. the goal is to generate patterns with similarity to all backgrounds. Experimental results and their evaluations demonstrate generated patterns inherited color and texture characteristics of all backgrounds. © 2011 IEEE.

关键词： C (programming language)

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Re-ranking Person Re-identification with k-reciprocal Encoding 30

Re-ranking Person Re-identification with <i>k</i>-reciprocal...

引用

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

作者： Zhong, Zhun Zheng, Liang Cao, Donglin Li, Shaozi Xiamen Univ Cognit Sci Dept Xiamen Peoples R China Univ Technol Sydney Sydney NSW Australia Xiamen Univ Fujian Key Lab Brain Inspired Comp Tech & Applica Xiamen Peoples R China

ISBN: (纸本)9781538604571

When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. the final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method(1).

关键词： Probes Encoding Measurement Feature extraction computer vision Robustness

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Deep Learning Face Representation from Predicting 10,000 Classes 27

Deep Learning Face Representation from Predicting 10,000 Cla...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Sun, Yi Wang, Xiaogang Tang, Xiaoou Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

this paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set. Moreover, the generalization capability of DeepID increases as more face classes are to be predicted at training. DeepID features are taken from the last hidden layer neuron activations of deep convolutional networks (ConvNets). When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons. the proposed features are extracted from various face regions to form complementary and over-complete representations. Any state-of-the-art classifiers can be learned based on these high-level representations for face verification. 97.45% verification accuracy on LFW is achieved with only weakly aligned faces.

关键词： deep learning face verification face verification Machine Learning Face Face Classifiers Feature extraction personal identity top layer Neurons

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