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Scale-aware alignment of hierarchical image segmentation

Scale-aware alignment of hierarchical image segmentation

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

作者： Chen, Yuhua Dai, Dengxin Pont-Tuset, Jordi Van Gool, Luc Computer Vision Lab. ETH Zurich Switzerland VISICS ESAT/PSI KU Leuven Belgium

ISBN: (纸本)9781467388511

Image segmentation is a key component in many computer vision systems, and it is recovering a prominent spot in the literature as methods improve and overcome their limitations. The outputs of most recent algorithms are in the form of a hierarchical segmentation, which provides segmentation at different scales in a single tree-like structure. Commonly, these hierarchical methods start from some lowlevel features, and are not aware of the scale information of the different regions in them. As such, one might need to work on many different levels of the hierarchy to find the objects in the scene. This work tries to modify the existing hierarchical algorithm by improving their alignment, that is, by trying to modify the depth of the regions in the tree to better couple depth and scale. To do so, we first train a regressor to predict the scale of regions using mid-level features. We then define the anchor slice as the set of regions that better balance between over-segmentation and undersegmentation. The output of our method is an improved hierarchy, re-aligned by the anchor slice. To demonstrate the power of our method, we perform comprehensive experiments, which show that our method, as a post-processing step, can significantly improve the quality of the hierarchical segmentation representations, and ease the usage of hierarchical image segmentation to high-level vision tasks such as object segmentation. We also prove that the improvement generalizes well across different algorithms and datasets, with a low computational cost.1.

关键词： Image segmentation

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Comparative deep learning of hybrid representations for image recommendations

Comparative deep learning of hybrid representations for imag...

引用

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

作者： Lei, Chenyi Liu, Dong Li, Weiping Zha, Zheng-Jun Li, Houqiang CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781467388511

In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two subnetworks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other stateof-the-art image recommendation solutions.

关键词： pattern recognition

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Efficient piecewise training of deep structured models for semantic segmentation

Efficient piecewise training of deep structured models for s...

引用

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

作者： Lin, Guosheng Shen, Chunhua Van Den Hengel, Anton Reid, Ian University of Adelaide Australian Centre for Robotic Vision Australia

ISBN: (纸本)9781467388511

Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information; specifically, we explore 'patch-patch' context between image regions, and 'patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an intersection-overunion score of 78.0 on the challenging PASCAL VOC 2012 dataset.

关键词： Image segmentation

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Homography estimation from the common self-polar triangle of separate ellipses

Homography estimation from the common self-polar triangle of...

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

作者： Huang, Haifei Zhang, Hui Cheung, Yiu-Ming Department of Computer Science Hong Kong Baptist University Hong Kong United International College BNU-HKBU Hong Kong

ISBN: (纸本)9781467388511

How to avoid ambiguity is a challenging problem for conic-based homography estimation. In this paper, we address the problem of homography estimation from two separate ellipses. We find that any two ellipses have a unique common self-polar triangle, which can provide three line correspondences. Furthermore, by investigating the location features of the common self-polar triangle, we show that one vertex of the triangle lies outside of both ellipses, while the other two vertices lies inside the ellipses separately. Accordingly, one more line correspondence can be obtained from the intersections of the conics and the common self-polar triangle. Therefore, four line correspondences can be obtained based on the common self-polar triangle, which can provide enough constraints for the homography estimation. The main contributions in this paper include: (1) A new discovery on the location features of the common self-polar triangle of separate ellipses. (2) A novel approach for homography estimation. Simulate experiments and real experiments are conducted to demonstrate the feasibility and accuracy of our approach.

关键词： Geometry

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PPP: Joint pointwise and pairwise image label prediction

PPP: Joint pointwise and pairwise image label prediction

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

作者： Wang, Yilin Wang, Suhang Tang, Jiliang Liu, Huan Li, Baoxin Department of Computer Science Arizona State Univerity United States Yahoo Research United States

ISBN: (纸本)9781467388511

Pointwise label and pairwise label are both widely used in computer vision tasks. For example, supervised image classification and annotation approaches use pointwise label, while attribute-based image relative learning often adopts pairwise labels. These two types of labels are often considered independently and most existing efforts utilize them separately. However, pointwise labels in image classification and tag annotation are inherently related to the pairwise labels. For example, an image labeled with "coast" and annotated with "beach, sea, sand, sky" is more likely to have a higher ranking score in terms of the attribute "open"; while "men shoes" ranked highly on the attribute "formal" are likely to be annotated with "leather, lace up" than "buckle, fabric". The existence of potential relations between pointwise labels and pairwise labels motivates us to fuse them together for jointly addressing related vision tasks. In particular, we provide a principled way to capture the relations between class labels, tags and attributes; and propose a novel framework PPP(Pointwise and Pairwise image label Prediction), which is based on overlapped group structure extracted from the pointwise-pairwise-label bipartite graph. With experiments on benchmark datasets, we demonstrate that the proposed framework achieves superior performance on three vision tasks compared to the state-of-the-art methods.

关键词： Image classification

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Temporal Multimodal learning in audiovisual speech recognition

Temporal Multimodal learning in audiovisual speech recogniti...

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

作者： Hu, Di Li, Xuelong Lu, Xiaoqiang Northwestern Polytechnical University Xi'an710072 China Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an710119 China

ISBN: (纸本)9781467388511

In view of the advantages of deep networks in producing useful representation, the generated features of different modality data (such as image, audio) can be jointly learned using Multimodal Restricted Boltzmann Machines (MRB-M). Recently, audiovisual speech recognition based the M-RBM has attracted much attention, and the MRBM shows its effectiveness in learning the joint representation across audiovisual modalities. However, the built networks have weakness in modeling the multimodal sequence which is the natural property of speech signal. In this paper, we will introduce a novel temporal multimodal deep learning architecture, named as Recurrent Temporal Multimodal RB-M (RTMRBM), that models multimodal sequences by transforming the sequence of connected MRBMs into a probabilistic series model. Compared with existing multimodal networks, it's simple and efficient in learning temporal joint representation. We evaluate our model on audiovisual speech datasets, two public (AVLetters and AVLetters2) and one self-build. The experimental results demonstrate that our approach can obviously improve the accuracy of recognition compared with standard MRBM and the temporal model based on conditional RBM. In addition, RTMRBM still outperforms non-temporal multimodal deep networks in the presence of the weakness of long-term dependencies.

关键词： Speech recognition

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Going deeper into first-person activity recognition

Going deeper into first-person activity recognition

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

作者： Ma, Minghuang Fan, Haoqi Kitani, Kris M. Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781467388511

We bring together ideas from recent work on feature design for egocentric action recognition under one framework by exploring the use of deep convolutional neural networks (CNN). Recent work has shown that features such as hand appearance, object attributes, local hand motion and camera ego-motion are important for characterizing first-person actions. To integrate these ideas under one framework, we propose a twin stream network architecture, where one stream analyzes appearance information and the other stream analyzes motion information. Our appearance stream encodes prior knowledge of the egocentric paradigm by explicitly training the network to segment hands and localize objects. By visualizing certain neuron activation of our network, we show that our proposed architecture naturally learns features that capture object attributes and hand-object configurations. Our extensive experiments on benchmark egocentric action datasets show that our deep architecture enables recognition rates that significantly outperform state-of-the-art techniques - an average 6.6% increase in accuracy over all datasets. Furthermore, by learning to recognize objects, actions and activities jointly, the performance of individual recognition tasks also increase by 30% (actions) and 14% (objects). We also include the results of extensive ablative analysis to highlight the importance of network design decisions.

关键词： Network architecture

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An empirical evaluation of current convolutional architectures' ability to managenuisance location and scale variability

An empirical evaluation of current convolutional architectur...

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

作者： Karianakis, Nikolaos Dong, Jingming Soatto, Stefano UCLA Vision Lab University of California Los AngelesCA90095 United States

ISBN: (纸本)9781467388511

We conduct an empirical study to test the ability of convolutional neural networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect ratio. We isolate factors by adopting a common convolutional architecture either deployed globally on the image to compute class posterior distributions, or restricted locally to compute class conditional distributions given location, scale and aspect ratios of bounding boxes determined by proposal heuristics. In theory, averaging the latter should yield inferior performance compared to proper marginalization. Yet empirical evidence suggests the converse, leading us to conclude that - at the current level of complexity of convolutional architectures and scale of the data sets used to train them - CNNs are not very effective at marginalizing nuisance variability. We also quantify the effects of context on the overall classification task and its impact on the performance of CNNs, and propose improved sampling techniques for heuristic proposal schemes that improve end-to-end performance to state-of-the-art levels. We test our hypothesis on a classification task using the ImageNet Challenge benchmark and on a wide-baseline matching task using the Oxford and Fischer's datasets.

关键词： Aspect ratio

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Augmented blendshapes for real-time simultaneous 3D head modeling and facial motion capture

Augmented blendshapes for real-time simultaneous 3D head mod...

引用

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

作者： Thomas, Diego Taniguchi, Rin-Ichiro Kyushu University Fukuoka Japan

ISBN: (纸本)9781467388511

We propose a method to build in real-time animated 3D head models using a consumer-grade RGB-D camera. Our framework is the first one to provide simultaneously comprehensive facial motion tracking and a detailed 3D model of the user's head. Anyone's head can be instantly reconstructed and his facial motion captured without requiring any training or pre-scanning. The user starts facing the camera with a neutral expression in the first frame, but is free to move, talk and change his face expression as he wills otherwise. The facial motion is tracked using a blendshape representation while the fine geometric details are captured using a Bump image mapped over the template mesh. We propose an efficient algorithm to grow and refine the 3D model of the head on-the-fly and in real-time. We demonstrate robust and high-fidelity simultaneous facial motion tracking and 3D head modeling results on a wide range of subjects with various head poses and facial expressions. Our proposed method offers interesting possibilities for animation production and 3D video telecommunications.

关键词： Three dimensional computer graphics

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NTU RGB+D: A large scale dataset for 3D human activity analysis

NTU RGB+D: A large scale dataset for 3D human activity analy...

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

作者： Shahroudy, Amir Liu, Jun Ng, Tian-Tsong Wang, Gang School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Institute for Infocomm Research Singapore Singapore

ISBN: (纸本)9781467388511

Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes. Currently available depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of training samples, distinct class labels, camera views and variety of subjects. In this paper we introduce a large-scale dataset for RGB+D human action recognition with more than 56 thousand video samples and 4 million frames, collected from 40 distinct subjects. Our dataset contains 60 different action classes including daily, mutual, and health-related actions. In addition, we propose a new recurrent neural network structure to model the long-term temporal correlation of the features for each body part, and utilize them for better action classification. Experimental results show the advantages of applying deep learning methods over state-of-the-art handcrafted features on the suggested cross-subject and crossview evaluation criteria for our dataset. The introduction of this large scale dataset will enable the community to apply, develop and adapt various data-hungry learning techniques for the task of depth-based and RGB+D-based human activity analysis.

关键词： computer vision

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