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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 ...

引用

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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Global context descriptors for SURF and MSER feature descriptors

Global context descriptors for SURF and MSER feature descrip...

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7th Canadian conference on computer and Robot vision, CRV 2010

作者： Carmichael, Gail Laganière, Robert Bose, Prosenjit School of Computer Science Carleton University Ottawa ON K1S 5B6 Canada VIVA Research Lab. SITE University of Ottawa Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9780769540405

Global context descriptors are vectors of additional information appended to an existing descriptor, and are computed as a log-polar histogram of nearby curvature values. these have been proposed in the past to make Scale Invariant Feature Transform (SIFT) matching more robust. this additional information improved matching results especially for images with repetitive features. We propose a similar global context descriptor for Speeded Up Robust Features (SURFs) and Maximally Stable Extremal Regions (MSERs). Our experiments show some improvement for SURFs when using the global context, and much improvement for MSER. © 2010 IEEE.

关键词： pattern recognition

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Combined articulatory and auditory processing for improved speech recognition

Combined articulatory and auditory processing for improved s...

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2012 7th IEEE conference on Industrial Electronics and Applications, ICIEA 2012

作者： Huang, Guangpu Er, Meng Joo Computer Vision Laboratory Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Singapore

ISBN: (纸本)9781457721175

In this paper, we examined the feasibility of articulatory phonetic inversion (API) conditioned on the auditory qualities for improved speech recognition. And we introduced an efficient data-driven heuristic learning algorithm to capture the articulatory-phonetic features (APFs) of English speech. then we reported the performance of the combined auditory and articulatory processing methods in the inversion and recognition experiments. Firstly, at the front end, the auditory based bark-frequency cepstral coefficient (BFCC) obtained equivalent or higher accuracy compared to the mel-frequency cepstral coefficient (MFCC). Secondly, the use of APFs also significantly altered the phoneme error patterns compared to the purely acoustic features, and they displayed advantages over the canonical pseudo-articulatory features (PAFs) which are manually derived from the phonological rules. the observations support our view that the combinational use of auditory and articulatory cues is beneficial for speech pattern classification. And the proposed neural based API model qualifies as a competitive candidate for profound phoneme recognition with salient features such as generality and portability. © 2012 IEEE.

关键词： Speech recognition

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Implementation of multimedia touch system with colour detection based computer vision algorithm

Implementation of multimedia touch system with colour detect...

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2012 7th International conference on Electrical and computer Engineering, ICECE 2012

作者： Rahman, Mohammad Wahidur Saha, Atanu Kumar Arafat, Muhammad Abdullah Nur, Rifat Al Islam, Rakibul Department of Electrical and Electronics Engineering Bangladesh University of Engineering and Technology Dhaka-1000 Bangladesh

ISBN: (纸本)9781467314367

Touch screen technology, as an user friendly interface for electronics device is going through a revolutionary improvement. Higher implementation cost makes this technology unsuitable for big display screen like multimedia display. the proposed technology in this paper provides touch screen facility to big multimedia display which is highly cost effective and easy to implement. An efficient colour detection based computer vision algorithm is used to recognize hand gesture pattern to execute different commands for interacting with multimedia system. © 2012 IEEE.

关键词： Gesture recognition

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the Fastest Deformable Part Model for Object Detection 27

The Fastest Deformable Part Model for Object Detection

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

作者： Yan, Junjie Lei, Zhen Wen, Longyin Li, Stan Z. Chinese Acad Sci Inst Automat Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

this paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in detection on challenging datasets. three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D correlation can be calculated by more efficient linear combination of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a discriminative manner. For cascade part pruning, neighborhood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order approximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orientation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the proposed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the proposed method achieves state-of-the-art accuracy on pedestrian and face detection task with frame-rate speed.

关键词： deformable part model face detection object detection Object detection Facial recognition digital power management Deformable models cascades Neighborhood tree surgery

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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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An iterated l₁ Algorithm for Non-smooth Non-convex Optimization in computer vision

An iterated <i>l</i><sub>1</sub> Algorithm for Non-smooth No...

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

作者： Ochs, Peter Dosovitskiy, Alexey Brox, thomas Pock, thomas Univ Freiburg Freiburg Germany Graz Univ Technol A-8010 Graz Austria

ISBN: (纸本)9780769549897

Natural image statistics indicate that we should use non-convex norms for most regularization tasks in image processing and computer vision. Still, they are rarely used in practice due to the challenge to optimize them. Recently, iteratively reweighed l(1) minimization has been proposed as a way to tackle a class of non-convex functions by solving a sequence of convex l(2)-l(1) problems. Here we extend the problem class to linearly constrained optimization of a Lipschitz continuous function, which is the sum of a convex function and a function being concave and increasing on the non-negative orthant (possibly non-convex and non-concave on the whole space). this allows to apply the algorithm to many computer vision tasks. We show the effect of non-convex regularizers on image denoising, deconvolution, optical flow, and depth map fusion. Non-convexity is particularly interesting in combination with total generalized variation and learned image priors. Efficient optimization is made possible by some important properties that are shown to hold.

关键词： computer vision concave programming convex programming deconvolution image denoising image sequences iterative methods minimisation

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Learning Expressionlets on Spatio-Temporal Manifold for Dynamic Facial Expression recognition 27

Learning Expressionlets on Spatio-Temporal Manifold for Dyna...

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

作者： Liu, Mengyi Shan, Shiguang Wang, Ruiping Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Oulu Dept Comp Sci & Engn SF-90100 Oulu Finland

ISBN: (纸本)9781479951178

Facial expression is temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account. In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e. expressionlet. Specifically, our method contains three key components: 1) each expression video clip is modeled as a spatio-temporal manifold (STM) formed by dense low-level features;2) a Universal Manifold Model (UMM) is learned over all low-level features and represented as a set of local ST modes to statistically unify all the STMs. 3) the local modes on each STM can be instantiated by fitting to UMM, and the corresponding expressionlet is constructed by modeling the variations in each local ST mode. With above strategy, expression videos are naturally aligned both spatially and temporally. To enhance the discriminative power, the expressionlet-based STM representation is further processed with discriminant embedding. Our method is evaluated on four public expression databases, CK+, MMI, Oulu-CASIA, and AFEW. In all cases, our method reports results better than the known state-of-the-art.

关键词： Semantics

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Multi-target Tracking by Rank-1 Tensor Approximation

Multi-target Tracking by Rank-1 Tensor Approximation

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

作者： Shi, Xinchu Ling, Haibin Xing, Junliang Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9780769549897

In this paper we formulate multi-target tracking (MTT) as a rank-1 tensor approximation problem and propose an l(1) norm tensor power iteration solution. In particular, a high order tensor is constructed based on trajectories in the time window, with each tensor element as the affinity of the corresponding trajectory candidate. the local assignment variables are the l(1) normalized vectors, which are used to approximate the rank-1 tensor. Our approach provides a flexible and effective formulation where both pairwise and high-order association energies can be used expediently. We also show the close relation between our formulation and the multi-dimensional assignment (MDA) model. To solve the optimization in the rank-1 tensor approximation, we propose an algorithm that iteratively powers the intermediate solution followed by an l(1) normalization. Aside from effectively capturing high-order motion information, the proposed solver runs efficiently with proved convergence. the experimental validations are conducted on two challenging datasets and our method demonstrates promising performances on both.

关键词： approximation theory computer vision human computer interaction iterative methods target tracking tensors vectors

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Multi-Level Discriminative Dictionary Learning towards Hierarchical Visual Categorization

Multi-Level Discriminative Dictionary Learning towards Hiera...

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

作者： Shen, Li Wang, Shuhui Sun, Gang Jiang, Shuqiang Huang, Qingming Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intell Info Proc Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China

ISBN: (纸本)9780769549897

For the task of visual categorization, the learning model is expected to be endowed with discriminative visual feature representation and flexibilities in processing many categories. Many existing approaches are designed based on a flat category structure, or rely on a set of pre-computed visual features, hence may not be appreciated for dealing with large numbers of categories. In this paper, we propose a novel dictionary learning method by taking advantage of hierarchical category correlation. For each internode of the hierarchical category structure, a discriminative dictionary and a set of classification models are learnt for visual categorization, and the dictionaries in different layers are learnt to exploit the discriminative visual properties of different granularity. Moreover, the dictionaries in lower levels also inherit the dictionary of ancestor nodes, so that categories in lower levels are described with multi-scale visual information using our dictionary learning approach. Experiments on ImageNet object data subset and SUN397 scene dataset demonstrate that our approach achieves promising performance on data with large numbers of classes compared with some state-of-the-art methods, and is more efficient in processing large numbers of categories.

关键词： dictionaries feature extraction image representation learning (artificial intelligence)

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