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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是21-30 订阅

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Super Normal Vector for Activity recognition Using Depth Sequences 27

Super Normal Vector for Activity Recognition Using Depth Seq...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yang, Xiaodong Tian, YingLi CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781479951178

this paper presents a new framework for human activity recognition from video sequences captured by a depth camera. We cluster hypersurface normals in a depth sequence to form the polynormal which is used to jointly characterize the local motion and shape information. In order to globally capture the spatial and temporal orders, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time grids. We then propose a novel scheme of aggregating the low-level polynormals into the super normal vector (SNV) which can be seen as a simplified version of the Fisher kernel representation. In the extensive experiments, we achieve classification results superior to all previous published results on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.

关键词： Classification (of information)

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Multilabel Ranking with Inconsistent Rankers 27

Multilabel Ranking with Inconsistent Rankers

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Geng, Xin Luo, Longrun Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479951178

While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where subjective inconsistent rankings from multiple rankers are associated with each instance. the key idea is to learn a latent preference distribution for each instance. the proposed method mainly includes two steps. the first step is to generate a common preference distribution that is most compatible to all the personal rankings. the second step is to learn a mapping from the instances to the preference distributions. the proposed preference distribution learning (PDL) method is applied to the problem of multilabel ranking for natural scene images. Experimental results show that PDL can effectively incorporate the information given by the inconsistent rankers, and perform remarkably better than the compared state-of-the-art multilabel ranking algorithms.

关键词： pattern recognition

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Strokelets: A Learned Multi-Scale Representation for Scene Text recognition 27

Strokelets: A Learned Multi-Scale Representation for Scene T...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yao, Cong Bai, Xiang Shi, Baoguang Liu, Wenyu Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781479951178

Driven by the wide range of applications, scene text detection and recognition have become active research topics in computer vision. though extensively studied, localizing and reading text in uncontrolled environments remain extremely challenging, due to various interference factors. In this paper, we propose a novel multi-scale representation for scene text recognition. this representation consists of a set of detectable primitives, termed as strokelets, which capture the essential substructures of characters at different granularities. Strokelets possess four distinctive advantages: (1) Usability: automatically learned from bounding box labels;(2) Robustness: insensitive to interference factors;(3) Generality: applicable to variant languages;and (4) Expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of strokelets and demonstrate the effectiveness of the proposed algorithm for text recognition.

关键词： Character recognition

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Efficient feature extraction, encoding and classification for action recognition 27

Efficient feature extraction, encoding and classification fo...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kantorov, Vadim Laptev, Ivan Ecole Normale Super INRIA WILLOW Dept Comp Sci Paris France

ISBN: (纸本)9781479951178

Local video features provide state-of-the-art performance for action recognition. While the accuracy of action recognition has been continuously improved over the recent years, the low speed of feature extraction and subsequent recognition prevents current methods from scaling up to real-size problems. We address this issue and first develop highly efficient video features using motion information in video compression. We next explore feature encoding by Fisher vectors and demonstrate accurate action recognition using fast linear classifiers. Our method improves the speed of video feature extraction, feature encoding and action classification by two orders of magnitude at the cost of minor reduction in recognition accuracy. We validate our approach and compare it to the state of the art on four recent action recognition datasets.

关键词： Feature extraction

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Collective Matrix Factorization Hashing for Multimodal Data 27

Collective Matrix Factorization Hashing for Multimodal Data

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ding, Guiguang Guo, Yuchen Zhou, Jile Tsinghua Univ Sch Software Beijing 100084 Peoples R China

ISBN: (纸本)9781479951178

Nearest neighbor search methods based on hashing have attracted considerable attention for effective and efficient large-scale similarity search in computer vision and information retrieval community. In this paper, we study the problems of learning hash functions in the context of multi-modal data for cross-view similarity search. We put forward a novel hashing method, which is referred to Collective Matrix Factorization Hashing (CMFH). CMFH learns unified hash codes by collective matrix factorization with latent factor model from different modalities of one instance, which can not only supports cross-view search but also increases the search accuracy by merging multiple view information sources. We also prove that CMFH, a similarity-preserving hashing learning method, has upper and lower boundaries. Extensive experiments verify that CMFH significantly outperforms several state-of-the-art methods on three different datasets.

关键词： hashing matrix decomposition images computer vision

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Gesture recognition Portfolios for Personalization 27

Gesture Recognition Portfolios for Personalization

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yao, Angela Van Gool, Luc Kohli, Pushmeet Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Louvain Belgium Microsoft Res Cambridge Cambridge England

ISBN: (纸本)9781479951178

Human gestures, similar to speech and handwriting, are often unique to the individual. Training a generic classifier applicable to everyone can be very difficult and as such, it has become a standard to use personalized classifiers in speech and handwriting recognition. In this paper, we address the problem of personalization in the context of gesture recognition, and propose a novel and extremely efficient way of doing personalization. Unlike conventional personalization methods which learn a single classifier that later gets adapted, our approach learns a set (portfolio) of classifiers during training, one of which is selected for each test subject based on the personalization data. We formulate classifier personalization as a selection problem and propose several algorithms to compute the set of candidate classifiers. Our experiments show that such an approach is much more efficient than adapting the classifier parameters but can still achieve comparable or better results.

关键词： classification gesture recognition personalization

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Learning Non-Linear Reconstruction Models for Image Set Classification 27

Learning Non-Linear Reconstruction Models for Image Set Clas...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hayat, Munawar Bennamoun, Mohammed An, Senjian Univ Western Australia Sch Comp Sci & Software Enginnering Nedlands WA 6009 Australia

ISBN: (纸本)9781479951178

We propose a deep learning framework for image set classification with application to face recognition. An Adaptive Deep Network Template (ADNT) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). the pre-initialized ADNT is then separately trained for images of each class and class-specific models are learnt. Based on the minimum reconstruction error from the learnt class-specific models, a majority voting strategy is used for classification. the proposed framework is extensively evaluated for the task of image set classification based face recognition on Honda/UCSD, CMU Mobo, YouTube Celebrities and a Kinect dataset. Our experimental results and comparisons with existing state-of-the-art methods show that the proposed method consistently achieves the best performance on all these datasets.

关键词： Deep Learning Face recognition Image Set Classification Facial recognition image set images reconstruction error Dataset Image Taxonomy optimum performance

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Learning Important Spatial Pooling Regions for Scene Classification 27

Learning Important Spatial Pooling Regions for Scene Classif...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lin, Di Lu, Cewu Liao, Renjie Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification. It is often caused by the complexity of latent image structure when convolving part filters with input images. this problem makes mid-level representation, even after pooling, not distinct enough to classify input data correctly to categories. Our solution is to learn important spatial pooling regions along with their appearance. the experiments show that this new framework suppresses false response and produces improved results on several datasets, including MIT-Indoor, 15-Scene, and UIUC 8-Sport. When combined with global image features, our method achieves state-of-the-art performance on these datasets.

关键词： pattern recognition

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Saliency Detection on Light Field 27

Saliency Detection on Light Field

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Li, Nianyi Ye, Jinwei Ji, Yu Ling, Haibin Yu, Jingyi Univ Delaware Newark DE 19716 USA Temple Univ Philadelphia PA 19122 USA

ISBN: (纸本)9781479951178

Existing saliency detection approaches use images as inputs and are sensitive to foreground/background similarities, complex background textures, and occlusions. We explore the problem of using light fields as input for saliency detection. Our technique is enabled by the availability of commercial plenoptic cameras that capture the light field of a scene in a single shot. We show that the unique refocusing capability of light fields provides useful focusness, depths, and objectness cues. We further develop a new saliency detection algorithm tailored for light fields. To validate our approach, we acquire a light field database of a range of indoor and outdoor scenes and generate the ground truth saliency map. Experiments show that our saliency detection scheme can robustly handle challenging scenarios such as similar foreground and background, cluttered background, complex occlusions, etc., and achieve high accuracy and robustness.

关键词： pattern recognition

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Leveraging Cognitive Context for Object recognition

Leveraging Cognitive Context for Object Recognition

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lawson, Wallace Hiatt, Laura Trafton, J. Gregory Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9781479943098

Contextual information can greatly improve both the speed and accuracy of object recognition. Context is most often viewed as a static concept, learned from large image databases. We build upon this concept by exploring cognitive context, demonstrating how rich dynamic context provided by computational cognitive models can improve object recognition. We demonstrate the use cognitive context to improve recognition using a small database of objects.

关键词： ACT-R Contextual Information LVis Neural Networks Object recognition

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