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CRAM: Compact representation of actions in movies

CRAM: Compact representation of actions in movies

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conference on computer vision and pattern recognition (CVPR)

作者： Mikel Rodriguez Computer Vision Lab University of Central Florida Orlando FL USA

Thousands of hours of video are recorded every second across the world. Due to the fact that searching for a particular event of interest within hours of video is time consuming, most captured videos are never examined, and are only used in a post-factum manner. In this work, we introduce activity-specific video summaries, which provide an effective means of browsing and indexing video based on a set of events of interest. Our method automatically generates a compact video representation of a long sequence, which features only activities of interest while preserving the general dynamics of the original video. Given a long input video sequence, we compute optical flow and represent the corresponding vector field in the Clifford Fourier domain. Dynamic regions within the flow field are identified within the phase spectrum volume of the flow field. We then compute the likelihood that certain activities of relevance occur within the the video by correlating it with spatio-temporal maximum average correlation height filters. Finally, the input sequence is condensed via a temporal shift optimization, resulting in a short video clip which simultaneously displays multiple instances of each relevant activity.

关键词： Motion pictures Image segmentation Biomembranes Image reconstruction Transmission electron microscopy Electron microscopy Animals Protocols Cost function Satellites

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Lymph node detection in 3-D chest CT using a spatial prior probability

Lymph node detection in 3-D chest CT using a spatial prior p...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Johannes Feulner S. Kevin Zhou Martin Huber Joachim Hornegger Dorin Comaniciu Alexander Cavallaro Siemens Corporate Technology Erlangen Germany Pattern Recognition Lab University of Erlangen Nuremberg Germany Siemens AG Corporate Research and Development Princeton NJ USA Radiology Institute University Hospital Erlangen Germany

Lymph nodes have high clinical relevance but detection is challenging as they are hard to see due to low contrast and irregular shape. In this paper, a method for fully automatic mediastinal lymph node detection in 3-D computed tomography (CT) images of the chest area is proposed. Discriminative learning is used to detect lymph nodes based on their appearance. Because lymph nodes can easily be confused with other structures, it is vital to incorporate as much anatomical knowledge as possible to achieve good detection rates. Here, a learned prior of the spatial distribution is proposed to model this knowledge. As atlas matching is generally inaccurate in the chest area because of anatomical variations, this prior is not learned in the space of a single atlas, but in the space of multiple ones that are attached to anatomical structures. During test, the priors are weighted and merged according to spatial distances. Cross-validation on 54 CT datasets showed that the prior based detector yields a true positive rate of 52.3% for seven false positives per volume image, which is about two times better than without a spatial prior.

关键词： Lymph nodes Computed tomography Detectors Cancer Filters Humans Shape Anatomical structure pattern recognition Radiology

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Sufficient dimension reduction for visual sequence classification

Sufficient dimension reduction for visual sequence classific...

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conference on computer vision and pattern recognition (CVPR)

作者： Alex Shyr Raquel Urtasun Michael I. Jordan University of California Berkeley USA TTI Chicago USA

When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensionality reduction can be employed to find a low-dimensional representation on which classification can be done more efficiently. Existing methods for supervised dimensionality reduction often presume that the data is densely sampled so that a neighborhood graph structure can be formed, or that the data arises from a known distribution. Sufficient dimension reduction techniques aim to find a low dimensional representation such that the remaining degrees of freedom become conditionally independent of the output values. In this paper we develop a novel sequence kernel dimension reduction approach (S-KDR). Our approach does not make strong assumptions on the distribution of the input data. Spatial, temporal and periodic information is combined in a principled manner, and an optimal manifold is learned for the end-task. We demonstrate the effectiveness of our approach on several tasks involving the discrimination of human gesture and motion categories, as well as on a database of dynamic textures.

关键词： Kernel computer vision Humans Principal component analysis Training data Spatial databases Unsupervised learning Linear discriminant analysis Gaussian processes Application software

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Harmony potentials for joint classification and segmentation

Harmony potentials for joint classification and segmentation

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conference on computer vision and pattern recognition (CVPR)

作者： Josep M. Gonfaus Xavier Boix Joost van de Weijer Andrew D. Bagdanov Joan Serrat Jordi Gonzàlez Centre de Visió per Computador Dept. of Computer Science Universitat Autònoma de Barcelona Spain

Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21.

关键词： Image classification Image segmentation Pixel computer science Object segmentation Image sampling Rendering (computer graphics) Histograms Image representation Acoustic noise

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Action unit detection with segment-based SVMs

Action unit detection with segment-based SVMs

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conference on computer vision and pattern recognition (CVPR)

作者： Tomas Simon Minh Hoai Nguyen Fernando De La Torre Jeffrey F. Cohn Robotics Institute Carnegie Mellon University USA

Automatic facial action unit (AU) detection from video is a long-standing problem in computer vision. Two main approaches have been pursued: (1) static modeling - typically posed as a discriminative classification problem in which each video frame is evaluated independently; (2) temporal modeling - frames are segmented into sequences and typically modeled with a variant of dynamic Bayesian networks. We propose a segment-based approach, kSeg-SVM, that incorporates benefits of both approaches and avoids their limitations. kSeg-SVM is a temporal extension of the spatial bag-of-words. kSeg-SVM is trained within a structured output SVM framework that formulates AU detection as a problem of detecting temporal events in a time series of visual features. Each segment is modeled by a variant of the BoW representation with soft assignment of the words based on similarity. Our framework has several benefits for AU detection: (1) both dependencies between features and the length of action units are modeled; (2) all possible segments of the video may be used for training; and (3) no assumptions are required about the underlying structure of the action unit events (e.g., i.i.d.). Our algorithm finds the best k-or-fewer segments that maximize the SVM score. Experimental results suggest that the proposed method outperforms state-of-the-art static methods for AU detection.

关键词： Gold Event detection Support vector machines Face detection computer vision Bayesian methods

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Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning

Towards weakly supervised semantic segmentation by means of ...

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conference on computer vision and pattern recognition (CVPR)

作者： Alexander Vezhnevets Joachim M. Buhmann ETH Zurich Zurich Switzerland

We address the task of learning a semantic segmentation from weakly supervised data. Our aim is to devise a system that predicts an object label for each pixel by making use of only image level labels during training - the information whether a certain object is present or not in the image. Such coarse tagging of images is faster and easier to obtain as opposed to the tedious task of pixelwise labeling required in state of the art systems. We cast this task naturally as a multiple instance learning (MIL) problem. We use Semantic Texton Forest (STF) as the basic framework and extend it for the MIL setting. We make use of multitask learning (MTL) to regularize our solution. Here, an external task of geometric context estimation is used to improve on the task of semantic segmentation. We report experimental results on the MSRC21 and the very challenging VOC2007 datasets. On MSRC21 dataset we are able, by using 276 weakly labeled images, to achieve the performance of a supervised STF trained on pixelwise labeled training set of 56 images, which is a significant reduction in supervision needed.

关键词： Pixel Image segmentation Labeling Tagging Bellows Testing Object segmentation Face detection computer vision Humans

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Learning full pairwise affinities for spectral segmentation

Learning full pairwise affinities for spectral segmentation

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conference on computer vision and pattern recognition (CVPR)

作者： Tae Hoon Kim Kyoung Mu Lee Sang Uk Lee Department of EECS ASRI Seoul National University Seoul South Korea

This paper studies the problem of learning a full range of pairwise affinities gained by integrating local grouping cues for spectral segmentation. The overall quality of the spectral segmentation depends mainly on the pairwise pixel affinities. By employing a semi-supervised learning technique, optimal affinities are learnt from the test image without iteration. We first construct a multi-layer graph with pixels and regions, generated by the mean shift algorithm, as nodes. By applying the semi-supervised learning strategy to this graph, we can estimate the intra- and inter-layer affinities between all pairs of nodes together. These pair-wise affinities are then used to simultaneously cluster all pixel and region nodes into visually coherent groups across all layers in a single multi-layer framework of Normalized Cuts. Our algorithm provides high-quality segmentations with object details by directly incorporating the full range connections in the spectral framework. Since the full affinity matrix is defined by the inverse of a sparse matrix, its eigen-decomposition is efficiently computed. The experimental results on Berkeley and MSRC image databases demonstrate the relevance and accuracy of our algorithm as compared to existing popular methods.

关键词： Image segmentation Pixel Clustering algorithms Semisupervised learning Sparse matrices Testing Image databases Humans Partitioning algorithms computer vision

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A novel Markov random field based deformable model for face recognition

A novel Markov random field based deformable model for face ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shu Liao Albert C.S. Chung Department of Computer Science and Engineering Lo Kwee-Seong Medical Image Analysis Laboratory Hong Kong University of Science and Technology Hong Kong China

In this paper, a new scheme to address the face recognition problem is proposed. Different from traditional face recognition approaches which represent each facial image by a single feature vector as the classification problem, the proposed method establishes a new way to formulate the face recognition problem as a deformable image registration problem. The main contributions of the paper lie in the following aspects: (i) Each pixel is represented by an anatomical feature signature calculated from its corresponding best scale salient region by using a new salient region detector based on the survival exponential entropy (SEE); (ii) The face recognition problem is formulated as a deformable image registration problem, the deformation model is represented by a Markov random field (MRF) labeling framework. Explicit pixel correspondence is established by the deformation framework. (iii) The survival exponential entropy based normalized mutual information (SEE-NMI) is proposed and integrated with the MRF based deformation model as the similarity measure to reflect the similarity between two facial images. The proposed method is evaluated on the FERET and FRGC version 2 databases and compared with several state-of-the-art face recognition approaches. Experimental results show that the proposed method achieves the highest recognition rate among all the compared approaches.

关键词： Markov random fields Deformable models Face recognition Image registration Pixel Entropy Face detection Detectors Labeling Mutual information

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ARISTA - image search to annotation on billions of web photos

ARISTA - image search to annotation on billions of web photo...

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conference on computer vision and pattern recognition (CVPR)

作者： Xin-Jing Wang Lei Zhang Ming Liu Yi Li Wei-Ying Ma Microsoft Research Asia Beijing China One Microsoft Way Microsoft Corporation Redmond USA

Though it has cost great research efforts for decades, object recognition is still a challenging problem. Traditional methods based on machine learning or computer vision are still in the stage of tackling hundreds of object categories. In recent years, non-parametric approaches have demonstrated great success, which understand the content of an image by propagating labels of its similar images in a large-scale dataset. However, due to the limited dataset size and imperfect image crawling strategy, previous work can only address a biased small subset of image concepts. Here we introduce the Arista project, which aims to build a practical image annotation engine targeting at popular concepts in the real world. In this project, we are particularly interested in understanding how many image concepts can be addressed by the data-driven annotation approach (coverage) and how good the performance is (precision). This paper reports the first stage of the work. Two billions web images were indexed, and based on simple yet effective near-duplicate detection, the system is capable of automatically generating accurate tags for popular web images having near-duplicates in the database. We found that about 8.1% web images have more than ten near duplicate and the number increases to 28.5% for top images in search results. Further, based on random samples in the latter case, we observed the precision of 57.9% at the point of the highest recall of 28% on ground truth tags.

关键词： Object recognition Image databases Image recognition Asia Costs Machine learning computer vision Large-scale systems Engines Surges

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Simultaneous searching of globally optimal interacting surfaces with shape priors

Simultaneous searching of globally optimal interacting surfa...

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conference on computer vision and pattern recognition (CVPR)

作者： Qi Song Xiaodong Wu Yunlong Liu Milan Sonka Mona Garvin Department of Electrical and Computer Engineering University of Iowa IA USA

Multiple surface searching with only image intensity information is a difficult job in the presence of high noise and weak edges. We present in this paper a novel method for globally optimal multi-surface searching with a shape prior represented by convex pairwise energies. A 3-D graph-theoretic framework is employed. An arc-weighted graph is constructed based on a shape model built from training datasets. A wide spectrum of constraints is then incorporated. The shape prior term penalizes the local topological change from the original shape model. The globally optimal solution for multiple surfaces can be obtained by computing a maximum flow in low-order polynomial time. Compared with other graph-based methods, our approach provides more local and flexible control of the shape. We also prove that our algorithm can handle the detection of multiple crossing surfaces with no shared voxels. Our method was applied to several application problems, including medical image segmentation, scenic image segmentation, and image resizing. Compared with results without using shape prior information, our improvement was quite impressive, demonstrating the promise of our method.

关键词： Image segmentation Shape control Biomedical imaging computer vision Optimization methods Cities and towns Multi-stage noise shaping Polynomials Change detection algorithms computer graphics

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