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Sparse coding for classification via discrimination ensemble

Sparse coding for classification via discrimination ensemble

引用

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

作者： Quan, Yuhui Xu, Yong Sun, Yuping Huang, Yan Ji, Hui School of Computer Science and Engineering South China Univ. of Tech. Guangzhou510006 China School of Automation Science and Engineering South China Univ. of Tech. Guangzhou510006 China Department of Mathematics National University of Singapore Singapore117542 Singapore

ISBN: (纸本)9781467388511

Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminability of sparse codes. Many existing approaches consider only a simple single linear classifier whose discriminative power is rather weak. In this paper, we proposed a discriminative sparse coding method which jointly learns a dictionary for sparse coding and an ensemble classifier for discrimination. The ensemble classifier is composed of a set of linear predictors and constructed via both subsampling on data and subspace projection on sparse codes. The advantages of the proposed method over the existing ones are multi-fold: better discriminability of sparse codes, weaker dependence on peculiarities of training data, and more expressibility of classifier for classification. These advantages are also justified in the experiments, as our method outperformed several recent methods in several recognition tasks.

关键词： Codes (symbols)

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Latent factor guided convolutional neural networks for age-invariant face recognition

Latent factor guided convolutional neural networks for age-i...

引用

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

作者： Wen, Yandong Li, Zhifeng Qiao, Yu School of Electronic and Information Engineering South China University of Technology China Shenzhen Key Lab of Comp. Vis. and Pat. Rec. Shenzhen Institutes of Advanced Technology CAS China

ISBN: (纸本)9781467388511

While considerable progresses have been made on face recognition, age-invariant face recognition (AIFR) still remains a major challenge in real world applications of face recognition systems. The major difficulty of AIFR arises from the fact that the facial appearance is subject to significant intra-personal changes caused by the aging process over time. In order to address this problem, we propose a novel deep face recognition framework to learn the ageinvariant deep face features through a carefully designed CNN model. To the best of our knowledge, this is the first attempt to show the effectiveness of deep CNNs in advancing the state-of-the-art of AIFR. Extensive experiments are conducted on several public domain face aging datasets (MORPH Album2, FGNET, and CACD-VS) to demonstrate the effectiveness of the proposed model over the state-ofthe-art. We also verify the excellent generalization of our new model on the famous LFW dataset.

关键词： Face recognition

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Occlusion-free face alignment: Deep Regression Networks coupled with de-corrupt autoencoders

Occlusion-free face alignment: Deep Regression Networks coup...

引用

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

作者： Zhang, Jie Kan, Meina Shan, Shiguang Chen, Xilin Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology China

ISBN: (纸本)9781467388511

Face alignment or facial landmark detection plays an important role in many computer vision applications, e.g., face recognition, facial expression recognition, face animation, etc. However, the performance of face alignment system degenerates severely when occlusions occur. In this work, we propose a novel face alignment method, which cascades several Deep Regression networks coupled with De-corrupt Autoencoders (denoted as DRDA) to explicitly handle partial occlusion problem. Different from the previous works that can only detect occlusions and discard the occluded parts, our proposed de-corrupt autoencoder network can automatically recover the genuine appearance for the occluded parts and the recovered parts can be leveraged together with those non-occluded parts for more accurate alignment. By coupling de-corrupt autoencoders with deep regression networks, a deep alignment model robust to partial occlusions is achieved. Besides, our method can localize occluded regions rather than merely predict whether the landmarks are occluded. Experiments on two challenging occluded face datasets demonstrate that our method significantly outperforms the state-of-the-art methods.

关键词： Face recognition

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Robust scene text recognition with automatic rectification

Robust scene text recognition with automatic rectification

引用

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

作者： Shi, Baoguang Wang, Xinggang Lyu, Pengyuan Yao, Cong Bai, Xiang School of Electronic Information and Communications Huazhong University of Science and Technology China

ISBN: (纸本)9781467388511

Recognizing text in natural images is a challenging task with many unsolved problems. Different from those in documents, words in natural images often possess irregular shapes, which are caused by perspective distortion, curved character placement, etc. We propose RARE (Robust text recognizer with Automatic REctification), a recognition model that is robust to irregular text. RARE is a speciallydesigned deep neural network, which consists of a Spatial Transformer Network (STN) and a Sequence recognition Network (SRN). In testing, an image is firstly rectified via a predicted Thin-Plate-Spline (TPS) transformation, into a more "readable" image for the following SRN, which recognizes text through a sequence recognition approach. We show that the model is able to recognize several types of irregular text, including perspective text and curved text. RARE is end-to-end trainable, requiring only images and associated text labels, making it convenient to train and deploy the model in practical systems. State-of-the-art or highly-competitive performance achieved on several benchmarks well demonstrates the effectiveness of the proposed model.

关键词： Character recognition

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Learning to match aerial images with deep attentive architectures

Learning to match aerial images with deep attentive architec...

引用

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

作者： Altwaijry, Hani Trulls, Eduard Hays, James Fua, Pascal Belongie, Serge Department of Computer Science Cornell University United States Cornell Tech United States Switzerland School of Interactive Computing College of Computing Georgia Institute of Technology United States

ISBN: (纸本)9781467388511

Image matching is a fundamental problem in computer vision. In the context of feature-based matching, SIFT and its variants have long excelled in a wide array of applications. However, for ultra-wide baselines, as in the case of aerial images captured under large camera rotations, the appearance variation goes beyond the reach of SIFT and RANSAC. In this paper we propose a data-driven, deep learning-based approach that sidesteps local correspondence by framing the problem as a classification task. Furthermore, we demonstrate that local correspondences can still be useful. To do so we incorporate an attention mechanism to produce a set of probable matches, which allows us to further increase performance. We train our models on a dataset of urban aerial imagery consisting of 'same' and 'different' pairs, collected for this purpose, and characterize the problem via a human study with annotations from Amazon Mechanical Turk. We demonstrate that our models outperform the state-of-the-art on ultra-wide baseline matching and approach human accuracy.

关键词： Deep learning

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Recurrently target-attending tracking

Recurrently target-attending tracking

引用

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

作者： Cui, Zhen Xiao, Shengtao Feng, Jiashi Yan, Shuicheng Research Center for Learning Science Southeast University Nanjing210096 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore 360 Artificial Intelligence Institute Beijing China

ISBN: (纸本)9781467388511

Robust visual tracking is a challenging task in computer vision. Due to the accumulation and propagation of estimation error, model drifting often occurs and degrades the tracking performance. To mitigate this problem, in this paper we propose a novel tracking method called Recurrently Target-attending Tracking (RTT). RTT attempts to identify and exploit those reliable parts which are beneficial for the overall tracking process. To bypass occlusion and discover reliable components, multi-directional Recurrent Neural Networks (RNNs) are employed in RTT to capture long-range contextual cues by traversing a candidate spatial region from multiple directions. The produced confidence maps from the RNNs are employed to adaptively regularize the learning of discriminative correlation filters by suppressing clutter background noises while making full use of the information from reliable parts. To solve the weighted correlation filters, we especially derive an efficient closedform solution with a sharp reduction in computation complexity. Extensive experiments demonstrate that our proposed RTT is more competitive over those correlation filter based methods.

关键词： Target tracking

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Fine-grained image classification by exploring bipartite-graph labels

Fine-grained image classification by exploring bipartite-gra...

引用

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

作者： Zhou, Feng Lin, Yuanqing NEC Labs United States

ISBN: (纸本)9781467388511

Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search by images, but it is very challenging because it needs to discern subtle difference between classes while dealing with the scarcity of training data. Fortunately, the ultra-fine granularity naturally brings rich relationships among object classes. This paper proposes a novel approach to exploit the rich relationships through bipartite-graph labels (BGL). We show how to model BGL in an overall convolutional neural networks and the resulting system can be optimized through back-propagation. We also show that it is computationally efficient in inference thanks to the bipartite structure. To facilitate the study, we construct a new food benchmark dataset, which consists of 37,885 food images collected from 6 restaurants and totally 975 menus. Experimental results on this new food and three other datasets demonstrate BGL advances previous works in fine-grained object recognition. An online demo is available at http://***/fg-demo/.

关键词： Object recognition

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Temporal epipolar regions

Temporal epipolar regions

引用

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

作者： Dar, Mor Moses, Yael Efi Arazi School of Computer Science Interdisciplinary Center Herzliya46150 Israel

ISBN: (纸本)9781467388511

Dynamic events are often photographed by a number of people from different viewpoints at different times, resulting in an unconstrained set of images. Finding the corresponding moving features in each of the images allows us to extract information about objects of interest in the scene. Computing correspondence of moving features in such a set of images is considerably more challenging than computing correspondence in video due to possible significant differences in viewpoints and inconsistent timing between image captures. The prediction methods used in video for improving robustness and efficiency are not applicable to a set of still images. In this paper we propose a novel method to predict locations of an approximately linear moving feature point, given a small subset of correspondences and the temporal order of image captures. Our method extends the use of epipolar geometry to divide images into valid and invalid regions, termed Temporal Epipolar Regions (TERs). We formally prove that the location of a feature in a new image is restricted to valid TERs. We demonstrate the effectiveness of our method in reducing the search space for correspondence on both synthetic and challenging real world data, and show the improved matching.

关键词： Image enhancement

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Slicing convolutional neural network for crowd video understanding

Slicing convolutional neural network for crowd video underst...

引用

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

作者： Shao, Jing Loy, Chen Change Kang, Kai Wang, Xiaogang Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781467388511

Learning and capturing both appearance and dynamic representations are pivotal for crowd video understanding. Convolutional Neural Networks (CNNs) have shown its remarkable potential in learning appearance representations from images. However, the learning of dynamic representation, and how it can be effectively combined with appearance features for video analysis, remains an open problem. In this study, we propose a novel spatio-temporal CNN, named Slicing CNN (S-CNN), based on the decomposition of 3D feature maps into 2D spatio- and 2D temporal-slices representations. The decomposition brings unique advantages: (1) the model is capable of capturing dynamics of different semantic units such as groups and objects, (2) it learns separated appearance and dynamic representations while keeping proper interactions between them, and (3) it exploits the selectiveness of spatial filters to discard irrelevant background clutter for crowd understanding. We demonstrate the effectiveness of the proposed S-CNN model on the WWW crowd video dataset for attribute recognition and observe significant performance improvements to the state-of-the-art methods (62.55% from 51.84% [21]).

关键词： pattern recognition

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Fashion style in 128 floats: Joint ranking and classification using weak data for feature extraction

Fashion style in 128 floats: Joint ranking and classificatio...

引用

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

作者： Simo-Serra, Edgar Ishikawa, Hiroshi Department of Computer Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9781467388511

We propose a novel approach for learning features from weakly-supervised data by joint ranking and classification. In order to exploit data with weak labels, we jointly train a feature extraction network with a ranking loss and a classification network with a cross-entropy loss. We obtain highquality compact discriminative features with few parameters, learned on relatively small datasets without additional annotations. This enables us to tackle tasks with specialized images not very similar to the more generic ones in existing fully-supervised datasets. We show that the resulting features in combination with a linear classifier surpass the state-of-the-art on the Hipster Wars dataset despite using features only 0.3% of the size. Our proposed features significantly outperform those obtained from networks trained on ImageNet, despite being 32 times smaller (128 singleprecision floats), trained on noisy and weakly-labeled data, and using only 1.5% of the number of parameters.1.

关键词： Classification (of information)

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