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Unsupervised feature selection using nonnegative spectral analysis 26

Unsupervised feature selection using nonnegative spectral an...

引用

26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： Li, Zechao Yang, Yi Liu, Jing Zhou, Xiaofang Lu, Hanqing National Lab. of Pattern Recognition Institute of Automation Chinese Academy of Science China School of Computer Science Carnegie Mellon University United States School of Information Technology and Electrical Engineering University of Queensland Australia

ISBN: (纸本)9781577355687

In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the cluster lab.ls of the input samples, during which the feature selection is performed simultaneously. The joint learning of the cluster lab.ls and feature selection matrix enables NDFS to select the most discriminative features. To learn more accurate cluster lab.ls, a nonnegative constraint is explicitly imposed to the class indicators. To reduce the redundant or even noisy features, 2,1-norm minimization constraint is added into the objective function, which guarantees the feature selection matrix sparse in rows. Our algorithm exploits the discriminative information and feature correlation simultaneously to select a better feature subset. A simple yet efficient iterative algorithm is designed to optimize the proposed objective function. Experimental results on different real world datasets demonstrate the encouraging performance of our algorithm over the state-of-the-arts. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Feature Selection

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RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax 1

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16th European Conference on computer vision, ECCV 2020

作者： Zhang, Xiao Zhao, Rui Qiao, Yu Li, Hongsheng CUHK-SenseTime Joint Lab The Chinese University of Hong Kong Hong Kong SenseTime Research Hong Kong ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9783030585747

ISBN: (纸本)9783030585730

Deep neural networks have achieved remarkable successes in learning feature representations for visual classification. However, deep features learned by the softmax cross-entropy loss generally show excessive intra-class variations. We argue that, because the traditional softmax losses aim to optimize only the relative differences between intra-class and inter-class distances (logits), it cannot obtain representative class prototypes (class weights/centers) to regularize intra-class distances, even when the training is converged. Previous efforts mitigate this problem by introducing auxiliary regularization losses. But these modified losses mainly focus on optimizing intra-class compactness, while ignoring keeping reasonable relations between different class prototypes. These lead to weak models and eventually limit their performance. To address this problem, this paper introduces a novel Radial Basis Function (RBF) distances to replace the commonly used inner products in the softmax loss function, such that it can adaptively assign losses to regularize the intra-class and inter-class distances by reshaping the relative differences, and thus creating more representative prototypes of classes to improve optimization. The proposed RBF-Softmax loss function not only effectively reduces intra-class distances, stabilizes the training behavior, and reserves ideal relations between prototypes, but also significantly improves the testing performance. Experiments on visual recognition benchmarks including MNIST, CIFAR-10/100, and ImageNet demonstrate that the proposed RBF-Softmax achieves better results than cross-entropy and other state-of-the-art classification losses. The code is at https://***/2han9x1a0release/RBF-Softmax. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Automatic detection and segmentation of focal liver lesions in contrast enhanced CT images

Automatic detection and segmentation of focal liver lesions ...

引用

International Conference on pattern recognition

作者： Militzer, Arne Hager, Tobias Jäger, Florian Tietjen, Christian Hornegger, Joachim Pattern Recognition Lab. Department of Computer Science Friedrich-Alexander-University Erlangen-Nuremberg Martensstr. 3 91058 Erlangen Germany Siemens AG Healthcare Sector Siemensstr. 1 91301 Forchheim Germany

ISBN: (纸本)9780769541099

In this paper a novel system for automatic detection and segmentation of focal liver lesions in CT images is presented. It utilizes a probabilistic boosting tree to classify points in the liver as either lesion or parenchyma, thus providing both detection and segmentation of the lesions at the same time and fully automatically. To make the segmentation more robust, an iterative classification scheme is integrated, that incorporates knowledge gained from earlier iterations into later decisions. Finally, a comprehensive evaluation of both the segmentation and the detection performance for the most common hypodense lesions is given. Detection rates of 77% could be achieved with a sensitivity of 0.95 and a specificity of 0.93 for lesion segmentation at the same settings. © 2010 IEEE.

关键词： Image segmentation

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A survey of historical document image datasets

A survey of historical document image datasets

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作者： Nikolaidou, Konstantina Seuret, Mathias Mokayed, Hamam Liwicki, Marcus EISLAB Machine Learning Group Luleå University of Technology Aurorum 1 Norrbotten Luleå97187 Sweden Pattern Recognition Lab Computer Vision Group Friedrich-Alexander-Universität Martensstr. 3 Bavaria Erlangen91058 Germany

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and lab.l representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies. © 2022, The Author(s).

关键词： Deterioration

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Facial landmark localization based on hierarchical pose regression with cascaded random ferns 13

Facial landmark localization based on hierarchical pose regr...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Zhanpeng Zhang, Wei Liu, Jianzhuang Tang, Xiaoou Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450324045

The main challenge of facial landmark localization in realworld application is that the large changes of head pose and facial expressions cause substantial image appearance variations. To avoid high dimensional regression in the 3D and 2D facial pose spaces simultaneously, we propose a hierarchical pose regression approach, estimating the head rotation, facial components and landmarks hierarchically. The regression process works in a unified cascaded fern framework. We present generalized gradient boosted ferns (GBFs) for the regression framework, which give better performance than traditional ferns. The framework also achieves real time performance. We verify our method on the latest benchmark datasets. The results show that it outperforms state-of-theart methods in both accuracy and speed. Copyright © 2013 ACM.

关键词： Regression analysis

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Learning to Predict Context-Adaptive Convolution for Semantic Segmentation 16th

Learning to Predict Context-Adaptive Convolution for Semanti...

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16th European Conference on computer vision, ECCV 2020

作者： Liu, Jianbo He, Junjun Qiao, Yu Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Hong Kong

ISBN: (纸本)9783030585945

Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can effectively improve the accuracy of semantic segmentation. However, the globally-sharing feature re-weighting vector might not be optimal for regions of different classes in the input image. In this paper, we propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector for each spatial location of the semantic feature maps. In CaC-Net, a set of context-adaptive convolution kernels are predicted from the global contextual information in a parameter-efficient manner. When used for convolution with the semantic feature maps, the predicted convolutional kernels can generate the spatially-varying feature weighting factors capturing both global and local contextual information. Comprehensive experimental results show that our CaC-Net achieves superior segmentation performance on three public datasets, PASCAL Context, PASCAL VOC 2012 and ADE20K. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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Towards a description for video indexation

Towards a description for video indexation

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International Conference on pattern recognition

作者： F. Lebourgeois J.-M. Jolion P.Ch. Awart Pattern Recognition & Vision Lab. Inst. Nat. des Sci. Appliquees Villeurbanne France

This paper presents the overall scheme of an indexation system for broadcast video designed for very large databases. We discuss features which can possibly be extracted from a video sequence so as to be used for "queries by example". We present examples of semantic features (query by content) like text reading, face localisation and classification. As this study is still at its infancy, we point out the key features and temporary achievements and present some temporary results.

关键词： Video sequences lab.ratories Multimedia communication Broadcasting Indexing Image storage Image color analysis pattern recognition Design automation Tellurium

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Multi-feature canonical correlation analysis for face photo-sketch image retrieval 13

Multi-feature canonical correlation analysis for face photo-...

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21st ACM International Conference on Multimedia, MM 2013

作者： Gong, Dihong Li, Zhifeng Liu, Jianzhuang Qiao, Yu Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Media Lab Huawei Technologies Co. Ltd. China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450324045

Automatic face photo-sketch image retrieval has attracted great attention in recent years due to its important applications in real life. The major difficulty in automatic face photo-sketch image retrieval lies in the fact that there exists great discrepancy between the different image modalities (photo and sketch). In order to reduce such discrepancy and improve the performance of automatic face photo-sketch image retrieval, we propose a new framework called multi-feature canonical correlation analysis (MCCA) to effectively address this problem. The MCCA is an extension and improvement of the canonical correlation analysis (CCA) algorithm using multiple features combined with two different random sampling methods in feature space and sample space. In this framework, we first represent each photo or sketch using a patch-based local feature representation scheme, in which histograms of oriented gradients (HOG) and multi-scale local binary pattern (MLBP) serve as the local descriptors. Canonical correlation analysis (CCA) is then performed on a collection of random subspaces to construct an ensemble of classifiers for photo-sketch image retrieval. Extensive experiments on two public-domain face photo-sketch datasets (CUFS and CUFSF) clearly show that the proposed approach obtains a substantial improvement over the state-of-the-art. Copyright © 2013 ACM.

关键词： Image retrieval

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Fast single image dehazing through Edge-Guided Interpolated Filter

Fast single image dehazing through Edge-Guided Interpolated ...

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IAPR International Conference on Machine vision Applications (MVA)

作者： Ximei Zhu Ying Li Yu Qiao Shenzhen Key lab of Computer Vision Pattern Recognition The Chinese University of Hong Kong Hong Kong SAR

ISBN: (纸本)9781479982479

Images and videos taken in foggy weather often suffer from low visibility. Recent studies demonstrate the effectiveness of dark channel prior [3] and guided filter [4] based approaches for image dehazing. However, these methods require high computational cost which makes them infeasible for realtime and embedding systems. In this paper, we propose Edge-Guided Interpolated Filter (EGIF) for fast image and video dehazing. The main contributions are twofold. Firstly, we develop Guided Interpolated Filter (GIF) to significantly speed up the estimation of transmission map, which is the most computational cost step in previous methods. Secondly, we utilize edge map as guidance image in GIF to enhance the fine details in dehazed images. Experimental results show that GIF can largely improve the computational efficiency and achieve comparable dehazing performance as previous guided filter based methods. EGIF can further enhance the sharpness of transmission map. Our method can achieve real-time processing for image of size 1024 × 768 with single CPU core (2GHz).

关键词： Image edge detection Interpolation Videos Real-time systems Image restoration computer vision Atmosphere

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The design and realization of a wearable embedded device for dietary and physical activity monitoring

The design and realization of a wearable embedded device for...

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3rd International Symposium on Systems and Control in Aeronautics and Astronautics, ISSCAA2010

作者： Zhang, Hong Li, Yuecheng Hackworth, Steven A. Yue, Yaofeng Li, Chengliu Yan, Guifang Sun, Mingui Image and Pattern Recognition Lab. School of Astronautics Beihang University 100191 China Departments of Neurosurgery Electrical and Computer Engineering and Bioengineering University of Pittsburgh Pittsburgh PA 15213 United States Department of Electrical and Computer Engineering University of Pittsburgh United States

ISBN: (纸本)9781424460441

This paper introduces the design and realization of a wearable device for dietary and physical activity monitoring. This design is based on technological advances in microelectronics and our previous prototypes which have verified the system concept. From our results, it is shown that the device meets all functional requirements as well as size and power consumption constraints essential to participants' use. With further technological development, we believe that the size of the system can be reduced and accurate and objective dietary assessments will become possible. ©2010 IEEE.

关键词： Wearable technology

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