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Equiangular kernel dictionary learning with applications to dynamic texture analysis

Equiangular kernel dictionary learning with applications to ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Quan, Yuhui Bao, Chenglong Ji, Hui School of Computer Science and Engineering South China Univ. of Tech. Guangzhou510006 China Department of Mathematics National University of Singapore Singapore117542 Singapore

ISBN: (纸本)9781467388511

Most existing dictionary learning algorithms consider a linear sparse model, which often cannot effectively characterize the nonlinear properties present in many types of visual data, e.g. dynamic texture (DT). Such nonlinear properties can be exploited by the so-called kernel sparse coding. This paper proposed an equiangular kernel dictionary learning method with optimal mutual coherence to exploit the nonlinear sparsity of high-dimensional visual data. Two main issues are addressed in the proposed method: (1) coding stability for redundant dictionary of infinite-dimensional space; and (2) computational efficiency for computing kernel matrix of training samples of high-dimensional data. The proposed kernel sparse coding method is applied to dynamic texture analysis with both local DT pattern extraction and global DT pattern characterization. The experimental results showed its performance gain over existing methods.

关键词： pattern recognition

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Guaranteed outlier removal with mixed integer linear programs

Guaranteed outlier removal with mixed integer linear program...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Chin, Tat-Jun Kee, Yang Heng Eriksson, Anders Neumann, Frank School of Computer Science University of Adelaide Australia School of Electrical Engineering and Computer Science Queensland University of Technology Australia

ISBN: (纸本)9781467388511

The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the problem exactly is computationally demanding, and the effort required increases rapidly with the problem size. Although randomized algorithms are much more efficient, the optimality of the solution is not guaranteed. Towards the goal of solving maximum consensus exactly, we present guaranteed outlier removal as a technique to reduce the runtime of exact algorithms. Specifically, before conducting global optimization, we attempt to remove data that are provably true outliers, i.e., those that do not exist in the maximum consensus set. We propose an algorithm based on mixed integer linear programming to perform the removal. The result of our algorithm is a smaller data instance that admits a much faster solution by subsequent exact algorithms, while yielding the same globally optimal result as the original problem. We demonstrate that overall speedups of up to 80% can be achieved on common vision problems1.

关键词： Integer programming

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Actor-action semantic segmentation with grouping process models

Actor-action semantic segmentation with grouping process mod...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Xu, Chenliang Corso, Jason J. Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

ISBN: (纸本)9781467388511

Actor-action semantic segmentation made an important step toward advanced video understanding: what action is happening; who is performing the action; and where is the action happening in space-time. Current methods based on layered CRFs for this problem are local and unable to capture the long-ranging interactions of video parts. We propose a new model that combines the labeling CRF with a supervoxel hierarchy, where supervoxels at various scales provide cues for possible groupings of nodes in the CRF to encourage adaptive and long-ranging interactions. The new model defines a dynamic and continuous process of information exchange: the CRF influences what supervoxels in the hierarchy are active, and these active supervoxels, in turn, affect the connectivities in the CRF; we hence call it a grouping process model. By further incorporating the video-level recognition, the proposed method achieves a large margin of 60% relative improvement over the state of the art on the recent A2D large-scale video labeling dataset, which demonstrates the effectiveness of our modeling.

关键词： computer vision

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Real-time depth refinement for specular objects

Real-time depth refinement for specular objects

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： El, Roy Or Hershkovitz, Rom Wetzler, Aaron Rosman, Guy Bruckstein, Alfred M. Kimmel, Ron Technion Israel Institute of Technology Israel Computer Science and Artificial Intelligence Lab MIT Israel

ISBN: (纸本)9781467388511

The introduction of consumer RGB-D scanners set off a major boost in 3D computer vision research. Yet, the precision of existing depth scanners is not accurate enough to recover fine details of a scanned object. While modern shading based depth refinement methods have been proven to work well with Lambertian objects, they break down in the presence of specularities. We present a novel shape from shading framework that addresses this issue and enhances both diffuse and specular objects' depth profiles. We take advantage of the built-in monochromatic IR projector and IR images of the RGB-D scanners and present a lighting model that accounts for the specular regions in the input image. Using this model, we reconstruct the depth map in real-time. Both quantitative tests and visual evaluations prove that the proposed method produces state of the art depth reconstruction results.

关键词： computer vision

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End-to-end learning of action detection from frame glimpses in videos

End-to-end learning of action detection from frame glimpses ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Yeung, Serena Russakovsky, Olga Mori, Greg Fei-Fei, Li Stanford University United States Carnegie Mellon University United States Simon Fraser University United States

ISBN: (纸本)9781467388511

In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the process of detecting actions is naturally one of observation and refinement: observing moments in video, and refining hypotheses about when an action is occurring. Based on this insight, we formulate our model as a recurrent neural network-based agent that interacts with a video over time. The agent observes video frames and decides both where to look next and when to emit a prediction. Since backpropagation is not adequate in this non-differentiable setting, we use REINFORCE to learn the agent's decision policy. Our model achieves state-of-the-art results on the THUMOS'14 and ActivityNet datasets while observing only a fraction (2% or less) of the video frames.

关键词： computer vision

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Recognizing micro-actions and reactions from paired egocentric videos

Recognizing micro-actions and reactions from paired egocentr...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Yonetani, Ryo Kitani, Kris M. Sato, Yoichi University of Tokyo Tokyo Japan Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781467388511

We aim to understand the dynamics of social interactions between two people by recognizing their actions and reactions using a head-mounted camera. Our work will impact several first-person vision tasks that need the detailed understanding of social interactions, such as automatic video summarization of group events and assistive systems. To recognize micro-level actions and reactions, such as slight shifts in attention, subtle nodding, or small hand actions, where only subtle body motion is apparent, we propose to use paired egocentric videos recorded by two interacting people. We show that the first-person and second-person points-of-view features of two people, enabled by paired egocentric videos, are complementary and essential for reliably recognizing micro-actions and reactions. We also build a new dataset of dyadic (two-persons) interactions that comprises more than 1000 pairs of egocentric videos to enable systematic evaluations on the task of micro-action and reaction recognition.

关键词： computer vision

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Direct prediction of 3D body poses from motion compensated sequences

Direct prediction of 3D body poses from motion compensated s...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Tekin, Bugra Rozantsev, Artem Lepetit, Vincent Fua, Pascal CVLab EPFL Lausanne Switzerland TU Graz Graz Austria

ISBN: (纸本)9781467388511

We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Previous approaches typically compute candidate poses in individual frames and then link them in a post-processing step to resolve ambiguities. By contrast, we directly regress from a spatio-temporal volume of bounding boxes to a 3D pose in the central frame. We further show that, for this approach to achieve its full potential, it is essential to compensate for the motion in consecutive frames so that the subject remains centered. This then allows us to effectively overcome ambiguities and improve upon the state-of-the-art by a large margin on the Human3.6m, HumanEva, and KTH Multiview Football 3D human pose estimation benchmarks.

关键词： computer vision

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Incremental object discovery in time-varying image collections

Incremental object discovery in time-varying image collectio...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Kontogianni, Theodora Mathias, Markus Leibe, Bastian Visual Computing Institute Computer Vision Group RWTH Aachen University Germany

ISBN: (纸本)9781467388511

In this paper, we address the problem of object discovery in time-varying, large-scale image collections. A core part of our approach is a novel Limited Horizon Minimum Spanning Tree (LH-MST) structure that closely approximates the Minimum Spanning Tree at a small fraction of the latter's computational cost. Our proposed tree structure can be created in a local neighborhood of the matching graph during image retrieval and can be efficiently updated whenever the image database is extended. We show how the LH-MST can be used within both single-link hierarchical agglomerative clustering and the Iconoid Shift framework for object discovery in image collections, resulting in significant efficiency gains and making both approaches capable of incremental clustering with online updates. We evaluate our approach on a dataset of 500k images from the city of Paris and compare its results to the batch version of both clustering algorithms.

关键词： Clustering algorithms

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How far are we from solving pedestrian detection?

How far are we from solving pedestrian detection?

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Shanshan Benenson, Rodrigo Omran, Mohamed Hosang, Jan Schiele, Bernt Max Planck Institute for Informatics Saarbrücken Germany

ISBN: (纸本)9781467388511

Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the "perfect single frame detector". We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector. Our results characterise both localisation and background-versusforeground errors. To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can improve even with a small portion of sanitised training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech dataset, and provide a new sanitised set of training and test annotations.

关键词： computer vision

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Event-specific image importance

Event-specific image importance

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wang, Yufei Lin, Zhe Shen, Xiaohui Mch, Radomír Miller, Gavin Cottrell, Garrison W. University of California San Diego United States Adobe Research United States

ISBN: (纸本)9781467388511

When creating a photo album of an event, people typically select a few important images to keep or share. There is some consistency in the process of choosing the important images, and discarding the unimportant ones. Modeling this selection process will assist automatic photo selection and album summarization. In this paper, we show that the selection of important images is consistent among different viewers, and that this selection process is related to the event type of the album. We introduce the concept of event-specific image importance. We collected a new event album dataset with human annotation of the relative image importance with each event album. We also propose a Convolutional Neural Network (CNN) based method to predict the image importance score of a given event album, using a novel rank loss function and a progressive training scheme. Results demonstrate that our method significantly outperforms various baseline methods.

关键词： computer vision

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