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检索条件"主题词=sparse coding"

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Supervised sparse coding with local geometrical constraints

Supervised sparse coding with local geometrical constraints

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： H. Zhang J. Xu Dept. of Comput. Sci. & Technol. East China Normal Univ. Shanghai China

ISBN: (纸本)9781467369985

sparse coding algorithms with geometrical constraints have received much attention recently. However, these methods are unsupervised and might lead to less discriminative representations. In this paper, we propose a supervised locality-constrained sparse coding method for classification. Two graphs are constructed, a labeled graph and an unlabeled graph. sparse codes with a labeled geometrical constraint will be more discriminative, however we cannot embed test samples with unknown label into a labeled graph. By coupling the two graphs, we aim to make the difference between sparse codes with labeled and unlabeled geometrical constraints as small as possible. As a result, sparse codes of test data can be obtained with the unlabeled geometrical constraint and the discrimination of the labeled geometrical constraint is maintained. Experiments on some benchmark datasets demonstrate the effectiveness of the proposed method.

关键词： geometrical constraint manifold embedding manifold learning sparse coding supervised

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sparse coding for Improved Signal-to-Noise Ratio in MRI 21

引用

21st International Conference on Neural Information Processing (ICONIP)

作者： Razzaq, Fuleah A. Mohamed, Shady Bhatti, Asim Nahavandi, Saeid Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia

ISBN: (纸本)9783319126432;9783319126425

Magnetic Resonance images (MRI) do not only exhibit sparsity but their sparsity take a certain predictable shape which is common for all kinds of images. That region based localised sparsity can be used to de-noise MR images from random thermal noise. This paper present a simple framework to exploit sparsity of MR images for image de-noising. As, noise in MR images tends to change its shape based on contrast level and signal itself, the proposed method is independent of noise shape and type and it can be used in combination with other methods.

关键词： Magnetic Resonance imaging(MRI) sparse coding Signal-to Noise Ratio (SNR) Additive White Gaussian Noise (AWGN)

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A novel extraction algorithm of video fingerprint based on sparse coding

A novel extraction algorithm of video fingerprint based on s...

引用

11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Jie Xu Xingfa Tian Tao Xu Jian Sun Key Lab of Optical Fiber Sensing and Communications (Ministry of Education) School of Communication and Information Engineering University of Electronic Science and Technology of China Chengdu 611731 China

ISBN: (纸本)9781510821279

By learning property of human fingerprint in bioinformatics, we used SURF to extract frames' features and handle them by visual vocabulary and word frequency analysis. Videos can be represented uniquely in this way. Based on the theory of image sparse coding in mammal's visual system, we used standard library to train sparse dictionary which could encode SURF features of frames. By dealing with nonzero value only, we found that the cost of storage and computation was reduced. The experiment and simulation showed that optimized result could maintain the robust of original features and had good discrimination and accuracy.

关键词： Feature Extract SURF sparse coding Video Fingerprint Visual Vocabulary sparse coding Fingerprints Videotapes Visual System FRAMES

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A Study on Efficient Transfer Learning for Reinforcement Learning using sparse coding

A Study on Efficient Transfer Learning for Reinforcement Lea...

引用

2015 7th International Conference on Computer Research and Development (ICCRD 2015)

作者： Midori Saito Ichiro Kobayashi Advanced Sciences Graduate School of Humanities and Sciences Ochanomizu University

By applying the knowledge previously obtained by reinforcement learning to new tasks, transfer learning has been successful in achieving efficient learning, rather than re-learning knowledge about action policies from scratch. However, in the case of applying transfer learning to reinforcement learning, it is not easy to determine which and how much the obtained knowledge should be transferred. With this background, in this study, we propose a novel method that enables to decide the knowledge and to determine the ratio of transference by adopting sparse coding in transfer learning. The transferred knowledge is represented as a linear combination of the accumulated knowledge by means of sparse coding. In the experiments, we have adopted colored mazes as tasks and confirmed that our proposed method significantly improved in terms of jumpstart and of the reduction of the total learning cost, compared with normal Q-learning.

关键词： sparse coding transfer learning reinforcement learning maze

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sparse coding Based Lip Texture Representation For Visual Speaker Identification 19

Sparse Coding Based Lip Texture Representation For Visual Sp...

引用

19th International Conference on Digital Signal Processing (DSP)

作者： Lai, Jun-Yao Wang, Shi-Lin Shi, Xing-Jian Liew, Alan Wee-Chung Shanghai Jiao Tong Univ Sch EIEE Shanghai 200030 Peoples R China Griffith Univ Sch Info & Comm Technol Brisbane Qld 4111 Australia

ISBN: (纸本)9781479946129

Recent research has shown that the speaker's lip shape and movement contain rich identity-related information and can be adopted for speaker identification and authentication. Among all the static lip features, the lip texture (intensity variation inside the outer lip contour) is of high discriminative power to differentiate various speakers. However, the existing lip texture feature representations cannot describe the texture information adequately and provide unsatisfactory identification results. In this paper, a sparse representation of the lip texture is proposed and a corresponding visual speaker identification scheme is presented. In the training stage, a sparse dictionary is built based on the texture samples for each speaker. In the testing stage, for any lip image investigated, the lip texture information is extracted and the reconstruction errors using all the dictionaries for every speaker are calculated. The lip image is identified to the speaker with the minimum reconstruction error. The experimental results show that the proposed sparse coding based scheme can achieve much better identification accuracy (91.37% for isolate image and 98.21% for image sequence) compared with several state-of-the-art methods when considering the lip texture information only.

关键词： Lip texture visual speaker identificaiton sparse coding lip biometrics

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Hierarchical sparse coding With Geometric Prior For Visual Geo-location

Hierarchical Sparse Coding With Geometric Prior For Visual G...

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Raghuraman Gopalan AT&T Labs-Research Dept. of Video and Multimedia Technologies Research

ISBN: (纸本)9781467369657

We address the problem of estimating location information of an image using principles from automated representation learning. We pursue a hierarchical sparse coding approach that learns features useful in discriminating images across locations, by initializing it with a geometric prior corresponding to transformations between image appearance space and their corresponding location grouping space using the notion of parallel transport on manifolds. We then extend this approach to account for the availability of heterogeneous data modalities such as geo-tags and videos pertaining to different locations, and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations. We evaluate our approach on several standard datasets such as im2gps, San Francisco and MediaEval2010, and obtain state-of-the-art results.

关键词： sparse coding initialization POSITION INFORMATION images Manifolds Imagery (Psychotherapy) Hierarchical

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Classification of Tiangong-1 hyperspectral remote sensing image via contextual sparse coding

Classification of Tiangong-1 hyperspectral remote sensing im...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qi Lv Yong Dou Xin Niu Jiaqing Xu Jinbo Xu School of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467372220

The hyperspectral remote sensing is one of the frontier techniques in the remote sensing research fields. Applying the sparse coding model to the hyperspectral remote sensing image processing is a hot topic in hyperspectral information processing. To improve the accuracy of hyperspectral image classification, we propose a classification method based on the spatial-spectral join-t contextual sparse coding. Firstly, a dictionary is obtained by training using samples selected from the ground-truth reference data. Then, the sparse coefficients of each pixel are calculated based on the learned dictionary. Afterward, the sparse coefficients are input to the classifier and the final classification result is obtained. The visible and near-infrared hyperspectral remote sensing image collected by Tiangong-1 in Chaoyang District of Beijing is used to evaluate the performance of the proposed approach. Experimental results show that the proposed method yields the best classification performance with the overall accuracy of 95.74% and the Kappa coefficient of 0.9476 in comparison with other classification methods.

关键词： Hyperspectral image Remote sensing sparse coding sparse coding Classification Methods remote sensing hyperspectral remote sensing hyperspectral imagery Image classification

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A sparse coding METHOD FOR SEMI-SUPERVISED SEGMENTATION WITH MULTI-CLASS HISTOGRAM CONSTRAINTS

A SPARSE CODING METHOD FOR SEMI-SUPERVISED SEGMENTATION WITH...

引用

IEEE International Conference on Image Processing

作者： Stefan Karnyaczki Christian Desrosiers Department of Software and IT Engineering Ecole de technologie superieure

ISBN: (纸本)9781479983407

We present a semi-supervised segmentation method that uses a dictionary of multi-class foreground histograms to enhance the segmentation in the presence of incorrect or missing labels. Instead of requiring a target histogram, or a set of images with the same foreground, this method uses sparse coding to find the most relevant histogram for the foreground. An efficient strategy based on the ADMM algorithm is proposed to avoid the problems of non-submodularity and non-linearity, normally related to histogram-based segmentation. Experiments on the segmentation of natural images with incomplete or incorrect labels show our method to be more robust and accurate than other approaches for this task.

关键词： Semi-supervised segmentation Histogram prior sparse coding sparse coding segmentation histograms Segmentation Dictionaries as Topic images

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Non-negative matrix factorisation incorporating greedy hellinger sparse coding applied to polyphonic music transcription

Non-negative matrix factorisation incorporating greedy helli...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： K. O'Hanlon M. Sandler M. D. Plumbley Centre for Digital Music Queen Mary Univ. of London London UK

ISBN: (纸本)9781467369985

Non-negative Matrix Factorisation (NMF) is a commonly used tool in many musical signal processing tasks, including Automatic Music Transcription (AMT). However unsupervised NMF is seen to be problematic in this context, and harmonically constrained variants of NMF have been proposed. While useful, the harmonic constraints may be constrictive in mixed signals. We have previously observed that recovery of overlapping signal elements using NMF is improved through introduction of a sparse coding step, and propose here the incorporation of a sparse coding step using the Hellinger distance into a NMF algorithm. Improved AMT results for unsupervised NMF are reported.

关键词： Hellinger distance Non-negative matrix factorisation music transcription sparse coding sparse coding NMF protocol matrix decomposition AMT gene Nonnegative

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Discriminant sparse coding with Geometrical Constraint

Discriminant Sparse Coding with Geometrical Constraint

引用

International Joint Conference on Neural Networks

作者： Hanchao Zhang Jinhua Xu Department of Computer Science and Technology East China Normal University 500 Dongchuan Road Shanghai

ISBN: (纸本)9781479919611

Recently, some sparse coding methods with geometrical constraint have been proposed, in which local geometrical structure of the data points was preserved during sparse coding process. These methods have been applied to classification problems and gained much success. However, they failed to use label information which has been proved to be useful in supervised sparse coding and discriminant manifold learning. In this paper, we propose a discriminant sparse coding approach with geometrical constraint. Labels are used to learn an intrinsic graph and a penalty graph, and these graphs are then embedded into sparse coding framework as constraints. The local geometric structure within each class is preserved and the separability between different classes is enforced. As a result, the discrimination of sparse coding will be improved. Experiments on benchmark databases demonstrate the effectiveness of the proposed method.

关键词： Discriminant sparse coding Geometrical Constraint local geometrical structure sparse coding Discriminant experimental data base

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