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Logistic boosting regression for label distribution learning?

Logistic boosting regression for label distribution learning...

引用

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

作者： Xing, Chao Geng, Xin Xue, Hui Key Lab of Computer Network and Information Integration Ministry of Education School of Computer Science and Engineering Southeast University Nanjing211189 China

ISBN: (纸本)9781467388511

Label Distribution Learning (LDL) is a general learning framework which includes both single label and multi-label learning as its special cases. One of the main assumptions made in traditional LDL algorithms is the derivation of the parametric model as the maximum entropy model. While it is a reasonable assumption without additional information, there is no particular evidence supporting it in the problem of LDL. Alternatively, using a general LDL model family to approximate this parametric model can avoid the potential influence of the specific model. In order to learn this general model family, this paper uses a method called Logistic Boosting Regression (LogitBoost) which can be seen as an additive weighted function regression from the statistical viewpoint. For each step, we can fit individual weighted regression function (base learner) to realize the optimization gradually. The base learners are chosen as weighted regression tree and vector tree, which constitute two algorithms named LDLogitBoost and AOSO-LDLogitBoost in this paper. Experiments on facial expression recognition, crowd opinion prediction on movies and apparent age estimation show that LDLogitBoost and AOSO-LDLogitBoost can achieve better performance than traditional LDL algorithms as well as other LogitBoost algorithms.

关键词： Face recognition

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Improved Hamming distance search using variable length Hashing

Improved Hamming distance search using variable length Hashi...

引用

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

作者： Ong, Eng-Jon Bober, Miroslaw Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9781467388511

This paper addresses the problem of ultra-large-scale search in Hamming spaces. There has been considerable research on generating compact binary codes in vision, for example for visual search tasks. However the issue of efficient searching through huge sets of binary codes remains largely unsolved. To this end, we propose a novel, unsupervised approach to thresholded search in Hamming space, supporting long codes (e.g. 512-bits) with a wide-range of Hamming distance radii. Our method is capable of working efficiently with billions of codes delivering between one to three orders of magnitude acceleration, as compared to prior art. This is achieved by relaxing the equal-size constraint in the Multi-Index Hashing approach, leading to multiple hash-tables with variable length hash-keys. Based on the theoretical analysis of the retrieval probabilities of multiple hash-tables we propose a novel search algorithm for obtaining a suitable set of hash-key lengths. The resulting retrieval mechanism is shown empirically to improve the efficiency over the state-of-the-art, across a range of datasets, bit-depths and retrieval thresholds.

关键词： Hamming distance

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Deep hand: How to train a CNN on 1 million hand images when your data is continuous and weakly labelled

Deep hand: How to train a CNN on 1 million hand images when ...

引用

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

作者： Koller, Oscar Ney, Hermann Bowden, Richard Human Language Technology and Pattern Recog. RWTH Aachen University Germany Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

ISBN: (纸本)9781467388511

This work presents a new approach to learning a framebased classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context of hand shape recognition, the approach has wider application to any video recognition task where frame level labelling is not available. The iterative EM algorithm leverages the discriminative ability of the CNN to iteratively refine the frame level annotation and subsequent training of the CNN. By embedding the classifier within an EM framework the CNN can easily be trained on 1 million hand images. We demonstrate that the final classifier generalises over both individuals and data sets. The algorithm is evaluated on over 3000 manually labelled hand shape images of 60 different classes which will be released to the community. Furthermore, we demonstrate its use in continuous sign language recognition on two publicly available large sign language data sets, where it outperforms the current state-of-the-art by a large margin. To our knowledge no previous work has explored expectation maximization without Gaussian mixture models to exploit weak sequence labels for sign language recognition.

关键词： Classification (of information)

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SketchNet: Sketch classification with web images

SketchNet: Sketch classification with web images

引用

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

作者： Zhang, Hua Liu, Si Zhang, Changqing Ren, Wenqi Wang, Rui Cao, Xiaochun Institute of Information Engineering Chinese Academy of Sciences China School of Computer Science and Technology Tianjin University China

ISBN: (纸本)9781467388511

In this study, we present a weakly supervised approach that discovers the discriminative structures of sketch images, given pairs of sketch images and web images. In contrast to traditional approaches that use global appearance features or relay on keypoint features, our aim is to automatically learn the shared latent structures that exist between sketch images and real images, even when there are significant appearance differences across its relevant real images. To accomplish this, we propose a deep convolutional neural network, named SketchNet. We firstly develop a triplet composed of sketch, positive and negative real image as the input of our neural network. To discover the coherent visual structures between the sketch and its positive pairs, we introduce the softmax as the loss function. Then a ranking mechanism is introduced to make the positive pairs obtain a higher score comparing over negative ones to achieve robust representation. Finally, we formalize above-mentioned constrains into the unified objective function, and create an ensemble feature representation to describe the sketch images. Experiments on the TU-Berlin sketch benchmark demonstrate the effectiveness of our model and show that deep feature representation brings substantial improvements over other state-of-the-art methods on sketch classification.

关键词： pattern recognition

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Blind image deconvolution by automatic gradient activation

Blind image deconvolution by automatic gradient activation

引用

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

作者： Gong, Dong Tan, Mingkui Zhang, Yanning Van Den Hengel, Anton Shi, Qinfeng School of Computer Science Northwestern Polytechnical University Xian China School of Computer Science University of Adelaide Australia

ISBN: (纸本)9781467388511

Blind image deconvolution is an ill-posed inverse problem which is often addressed through the application of appropriate prior. Although some priors are informative in general, many images do not strictly conform to this, leading to degraded performance in the kernel estimation. More critically, real images may be contaminated by nonuniform noise such as saturation and outliers. Methods for removing specific image areas based on some priors have been proposed, but they operate either manually or by defining fixed criteria. We show here that a subset of the image gradients are adequate to estimate the blur kernel robustly, no matter the gradient image is sparse or not. We thus introduce a gradient activation method to automatically select a subset of gradients of the latent image in a cutting-plane-based optimization scheme for kernel estimation. No extra assumption is used in our model, which greatly improves the accuracy and flexibility. More importantly, the proposed method affords great convenience for handling noise and outliers. Experiments on both synthetic data and real-world images demonstrate the effectiveness and robustness of the proposed method in comparison with the state-of-the-art methods.

关键词： Inverse problems

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Picking deep filter responses for fine-grained image recognition

Picking deep filter responses for fine-grained image recogni...

引用

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

作者： Zhang, Xiaopeng Xiong, Hongkai Zhou, Wengang Lin, Weiyao Tian, Qi Shanghai Jiao Tong University China University of Science and Technology of China China University of Texas at San Antonio United States

ISBN: (纸本)9781467388511

Recognizing fine-grained sub-categories such as birds and dogs is extremely challenging due to the highly localized and subtle differences in some specific parts. Most previous works rely on object/part level annotations to build part-based representation, which is demanding in practical applications. This paper proposes an automatic finegrained recognition approach which is free of any object/part annotation at both training and testing stages. Our method explores a unified framework based on two steps of deep filter response picking. The first picking step is to find distinctive filters which respond to specific patterns significantly and consistently, and learn a set of part detectors via iteratively alternating between new positive sample mining and part model retraining. The second picking step is to pool deep filter responses via spatially weighted combination of Fisher Vectors. We conditionally pick deep filter responses to encode them into the final representation, which considers the importance of filter responses themselves. Integrating all these techniques produces a much more powerful framework, and experiments conducted on CUB-200-2011 and Stanford Dogs demonstrate the superiority of our proposed algorithm over the existing methods.

关键词： pattern recognition

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Learning transferrable knowledge for semantic segmentation with deep convolutional neural network

Learning transferrable knowledge for semantic segmentation w...

引用

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

作者： Hong, Seunghoon Oh, Junhyuk Lee, Honglak Han, Bohyung Dept. of Computer Science and Engineering POSTECH Pohang Korea Republic of Dept. of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

ISBN: (纸本)9781467388511

We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation annotations available for different categories to guide segmentations on images with only image-level class labels. To make segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. In this architecture, the model generates spatial highlights of each category presented in images using an attention model, and subsequently performs binary segmentation for each highlighted region using decoder. Combining attention model, the decoder trained with segmentation annotations in different categories boosts accuracy of weakly-supervised semantic segmentation. The proposed algorithm demonstrates substantially improved performance compared to the state-of-theart weakly-supervised techniques in PASCAL VOC 2012 dataset when our model is trained with the annotations in 60 exclusive categories in Microsoft COCO dataset.

关键词： Image segmentation

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Sparsifying neural network connections for face recognition

Sparsifying neural network connections for face recognition

引用

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

作者： Sun, Yi Wang, Xiaogang Tang, Xiaoou SenseTime Group Hong Kong Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781467388511

This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition. The sparse ConvNets are learned in an iterative way, each time one additional layer is sparsified and the entire model is re-trained given the initial weights learned in previous iterations. One important finding is that directly training the sparse ConvNet from scratch failed to find good solutions for face recognition, while using a previously learned denser model to properly initialize a sparser model is critical to continue learning effective features for face recognition. This paper also proposes a new neural correlation-based weight selection criterion and empirically verifies its effectiveness in selecting informative connections from previously learned models in each iteration. When taking a moderately sparse structure (26%-76% of weights in the dense model), the proposed sparse ConvNet model significantly improves the face recognition performance of the previous state-of-theart DeepID2+ models given the same training data, while it keeps the performance of the baseline model with only 12% of the original parameters.

关键词： Face recognition

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Online detection and classification of dynamic hand gestures with recurrent 3D convolutional neural networks

Online detection and classification of dynamic hand gestures...

引用

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

作者： Molchanov, Pavlo Yang, Xiaodong Gupta, Shalini Kim, Kihwan Tyree, Stephen Kautz, Jan NVIDIA Korea Republic of

ISBN: (纸本)9781467388511

Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as:1) there is a large diversity in how people perform gestures, making detection and classification difficult; 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification; in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous. In this paper, we address these challenges with a recurrent three-dimensional convolutional neural network that performs simultaneous detection and classification of dynamic hand gestures from multi-modal data. We employ connectionist temporal classification to train the network to predict class labels from inprogress gestures in unsegmented input streams. In order to validate our method, we introduce a new challenging multimodal dynamic hand gesture dataset captured with depth, color and stereo-IR sensors. On this challenging dataset, our gesture recognition system achieves an accuracy of 83.8%, outperforms competing state-of-the-art algorithms, and approaches human accuracy of 88.4%. Moreover, our method achieves state-of-the-art performance on SKIG and ChaLearn2014 benchmarks.

关键词： Gesture recognition

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Camera calibration from periodic motion of a pedestrian

Camera calibration from periodic motion of a pedestrian

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

作者： Huang, Shiyao Ying, Xianghua Rong, Jiangpeng Shang, Zeyu Zha, Hongbin School of Electronic Engineering and Computer Science Center for Information Science Peking University Beijing100871 China

ISBN: (纸本)9781467388511

Camera calibration directly from image sequences of a pedestrian without using any calibration object is a really challenging task and should be well solved in computer vision, especially in visual surveillance. In this paper, we propose a novel camera calibration method based on recovering the three orthogonal vanishing points (TOVPs), just using an image sequence of a pedestrian walking in a straight line, without any assumption of scenes or motions, e.g., control points with known 3D coordinates, parallel or perpendicular lines, non-natural or pre-designed special human motions, as often necessary in previous methods. The traces of shoes of a pedestrian carry more rich and easily detectable metric information than all other body parts in the periodic motion of a pedestrian, but such information is usually overlooked by previous work. In this paper, we employ the images of the toes of the shoes on the ground plane to determine the vanishing point corresponding to the walking direction, and then utilize harmonic conjugate properties in projective geometry to recover the vanishing point corresponding to the perpendicular direction of the walking direction in the horizontal plane and the vanishing point corresponding to the vertical direction. After recovering all of the TOVPs, the intrinsic and extrinsic parameters of the camera can be determined. Experiments on various scenes and viewing angles prove the feasibility and accuracy of the proposed method.

关键词： computer vision

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