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Learning Mid-level Filters for Person Re-identification 27

Learning Mid-level Filters for Person Re-identification

引用

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

作者： Zhao, Rui Ouyang, Wanli Wang, Xiaogang Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. third, filter responses are integrated with patch matching scores in RankSVM training. the effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. the learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%.

关键词： Mid-level filter person re-identification

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Motion-Depth: RGB-D Depth Map Enhancement with Motion and Depth in Complement 27

Motion-Depth: RGB-D Depth Map Enhancement with Motion and De...

引用

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

作者： Hui, Tak-Wai Ngan, King Ngi Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Low-cost RGB-D imaging system such as Kinect is widely utilized for dense 3D reconstruction. However, RGB-D system generally suffers from two main problems. the spatial resolution of the depth image is low. the depth image often contains numerous holes where no depth measurements are available. this can be due to bad infra-red reflectance properties of some objects in the scene. Since the spatial resolution of the color image is generally higher than that of the depth image, this paper introduces a new method to enhance the depth images captured by a moving RGB-D system using the depth cues from the induced optical flow. We not only fill the holes in the raw depth images, but also recover fine details of the imaged scene. We address the problem of depth image enhancement by minimizing an energy functional. In order to reduce the computational complexity, we have treated the textured and homogeneous regions in the color images differently. Experimental results on several RGB-D sequences are provided to show the effectiveness of the proposed method.

关键词： Image enhancement

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Constructing Robust Affinity Graphs for Spectral Clustering 27

Constructing Robust Affinity Graphs for Spectral Clustering

引用

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

作者： Zhu, Xiatian Loy, Chen Change Gong, Shaogang Queen Mary Univ London London E1 4NS England Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Spectral clustering requires robust and meaningful affinity graphs as input in order to form clusters with desired structures that can well support human intuition. To construct such affinity graphs is non-trivial due to the ambiguity and uncertainty inherent in the raw data. In contrast to most existing clustering methods that typically employ all available features to construct affinity matrices with the Euclidean distance, which is often not an accurate representation of the underlying data structures, we propose a novel unsupervised approach to generating more robust affinity graphs via identifying and exploiting discriminative features for improving spectral clustering. Specifically, our model is capable of capturing and combining subtle similarity information distributed over discriminative feature subspaces for more accurately revealing the latent data distribution and thereby leading to improved data clustering, especially with heterogeneous data sources. We demonstrate the efficacy of the proposed approach on challenging image and video datasets.

关键词： Robust affinity graphs discriminative feature subspaces random forests spectral clustering subtle similarity weak proximity

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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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Image-based Synthesis and Re-Synthesis of Viewpoints Guided by 3D Models 27

Image-based Synthesis and Re-Synthesis of Viewpoints Guided ...

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

作者： Rematas, Konstantinos Ritschel, Tobias Fritz, Mario Tuytelaars, Tinne Katholieke Univ Leuven IMinds Louvain Belgium Max Planck Inst Informat Munich Germany Univ Saarland D-66123 Saarbrucken Germany

ISBN: (纸本)9781479951178

We propose a technique to use the structural information extracted from a set of 3D models of an object class to improve novel-view synthesis for images showing unknown instances of this class. these novel views can be used to "amplify" training image collections that typically contain only a low number of views or lack certain classes of views entirely (e. g. top views). We extract the correlation of position, normal, reflectance and appearance from computer-generated images of a few exemplars and use this information to infer new appearance for new instances. We show that our approach can improve performance of state-of-the-art detectors using real-world training data. Additional applications include guided versions of inpainting, 2D-to-3D conversion, super-resolution and non-local smoothing.

关键词： computer graphics computer vision image-based rendering object detection synthetic training data

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Empirical Minimum Bayes Risk Prediction: How to extract an extra few% performance from vision models with just three more parameters 27

Empirical Minimum Bayes Risk Prediction: How to extract an e...

引用

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

作者： Premachandran, Vittal Tarlow, Daniel Batra, Dhruv Natl Univ Singapore Singapore 117548 Singapore Microsoft Res Redmond WA USA Virginia Tech Blacksburg VA 24061 USA

ISBN: (纸本)9781479951178

When building vision systems that predict structured objects such as image segmentations or human poses, a crucial concern is performance under task-specific evaluation measures (e.g. Jaccard Index or Average Precision). An ongoing research challenge is to optimize predictions so as to maximize performance on such complex measures. In this work, we present a simple meta-algorithm that is surprisingly effective - Empirical Min Bayes Risk. EMBR takes as input a pre-trained model that would normally be the final product and learns three additional parameters so as to optimize performance on the complex high-order task-specific measure. We demonstrate EMBR in several domains, taking existing state-of-the-art algorithms and improving performance up to similar to 7%, simply with three extra parameters.

关键词： Image segmentation

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A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions 27

A Probabilistic Framework for Multitarget Tracking with Mutu...

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

作者： Yang, Menglong Liu, Yiguang Wen, Longyin You, Zhisheng Li, Stan Z. Sichuan Univ Key Lab Fundamental Synthet Vis Graph & Image Nat Sch Aeronaut & Astronaut & Comp Sci Chengdu Peoples R China Chinese Acad Sci Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Mutual occlusions among targets can cause track loss or target position deviation, because the observation likelihood of an occluded target may vanish even when we have the estimated location of the target. this paper presents a novel probability framework for multitarget tracking with mutual occlusions. the primary contribution of this work is the introduction of a vectorial occlusion variable as part of the solution. the occlusion variable describes occlusion states of the targets. this forms the basis of the proposed probability framework, with the following further contributions: 1) Likelihood: A new observation likelihood model is presented, in which the likelihood of an occluded target is computed by referring to both of the occluded and occluding targets. 2) Priori: Markov random field (MRF) is used to model the occlusion priori such that less likely "circular" or "cascading" types of occlusions have lower priori probabilities. Both the occlusion priori and the motion priori take into consideration the state of occlusion. 3) Optimization: A realtime RJMCMC-based algorithm with a new move type called "occlusion state update" is presented. Experimental results show that the proposed framework can handle occlusions well, even including long-duration full occlusions, which may cause tracking failures in the traditional methods.

关键词： Markov processes

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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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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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Clothing Co-Parsing by Joint Image Segmentation and Labeling 27

Clothing Co-Parsing by Joint Image Segmentation and Labeling

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

作者： Yang, Wei Luo, Ping Lin, Liang Sun Yat Sen Univ Guangzhou 510275 Guangdong Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China SYSU CMU Shunde Int Joint Res Inst Shunde Peoples R China

ISBN: (纸本)9781479951178

this paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. the first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (ESVM) technique [23]. In the second phase (i.e. "region colabeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). the joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.

关键词： Clothing recognition EM Algorithm Human Parsing Image Understand

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