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检索条件"主题词=sparse coding"

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Label Propagation and Soft-similarity Measure for Graph based Constrained Semi-Supervised Learning

Label Propagation and Soft-similarity Measure for Graph base...

引用

International Joint Conference on Neural Networks

作者： Zhao Zhang Mingbo Zhao Tommy W. S. Chow School of Computer Science and Technology Soochow University Department of Electronic Engineering City University of Hong Kong

ISBN: (纸本)9781479914821

This paper discusses a new setting of graph based semi-supervised learning (SSL) guided using pairwise constraints (PCs). Technically, we propose a novel Graph based Constrained Semi-Supervised Learning (G-CSSL) framework. In this setting, PCs are used to specify the types (intra- or inter-class) of points with labels. Because the number of labeled data is typically small in SSL setting, the core idea of this framework is to create and enrich the PCs sets using the propagated soft labels from both labeled and unlabeled data via special label propagation (SLP), and hence obtaining more supervised information for delivering enhanced learning performance. To obtain the predicted labels of unlabeled data, we calculate the sparse codes of all data vectors jointly to assign weights for SLP. To deliver enhanced inter-class separation and intra-class compactness, we also present a mixed soft-similarity measure to evaluate the similarity/dissimilarity of constrained sample pairs by using the sparse codes and outputted probabilistic values by SLP. Extensive simulations demonstrated the effectiveness of our G-CSSL for image representation and recognition, compared with other related SSL techniques.

关键词： Label propagation Soft-similarity measure sparse coding Constrained semi-supervised learning Subspace learning

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Reconstruction of Compressively Sensed Images using Regularized sparse Dictionary Learning and Adaptive Spectral Filtering

Reconstruction of Compressively Sensed Images using Regulari...

引用

IEEE International Symposium on Multimedia

作者： Amol Mangirish Singbal Nishant Deepak Keni Rizwan Ahmed Ansari School of Electrical & Comp. Engg Georgia Institute of Technology Atlanta Georgia U.S.A. Environmental Earth and Geospatial Sciences NCCU North Carolina U.S.A.

ISBN: (数字)9781728156064

ISBN: (纸本)9781728156071

sparse representation using over-complete dictionaries have shown to produce good quality results in various image processing tasks. Dictionary learning algorithms have made it possible to engineer data-adaptive dictionaries that have promising applications in image compression and image enhancement. The most common sparse dictionary learning algorithms use the techniques of matching pursuit and K-SVD iteratively for sparse coding and dictionary learning respectively. While this technique produces good results, it requires a large number of iterations to converge to an optimal solution. In this article, we use a closed-form stabilized convex optimization technique for both sparse coding and dictionary learning. The approach results in providing the best possible dictionary and the sparsest representation resulting in minimum reconstruction error. We have used the proposed algorithm for compressed sensing of satellite images. Once the image is reconstructed from the compressively sensed samples, we use adaptive spatial and frequency domain filtering techniques to move towards exact image recovery. It is seen from the results that the proposed algorithm provides much better reconstruction results than conventional sparse dictionary techniques for a fixed number of iterations. Depending upon the number of details present in the image, the proposed algorithm is seen to reach the optimal solution with a significantly lower number of iterations. Consequently, high PSNR and low MSE is obtained using the proposed algorithm for our compressive sensing framework.

关键词： sparse representation Dictionary learning Compressed sensing Tikhonov regularization Ridge regression Convex optimization Compressed sensing sparse representation Dictionaries as Topic ridge regression Convex function sparse coding Image compression Data compression iterative methods optimal solution Reconstructive Surgical Procedures images

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Learning Bound for Parameter Transfer Learning 16

Learning Bound for Parameter Transfer Learning

引用

Annual Conference on Neural Information Processing Systems

作者： Wataru Kumagai Faculty of Engineering Kanagawa University

ISBN: (纸本)9781510838819

We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another objective task. Then, we introduce the notion of the local stability and parameter transfer learnability of parametric feature mapping, and thereby derive a learning bound for parameter transfer algorithms. As an application of parameter transfer learning, we discuss the performance of sparse coding in self-??taught learning. Although self-taught learning algorithms with plentiful unlabeled data often show excellent empirical performance, their theoretical analysis has not been studied. In this paper, we also provide the first theoretical learning bound for self-taught learning.

关键词： Learning feature map Transfer (Psychology) sparse coding local stability Bound Theoretical analysis

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DETECTOR ANOMALY DETECTION AND STRIPE CORRECTION OF HYPERSPECTRAL DATA

DETECTOR ANOMALY DETECTION AND STRIPE CORRECTION OF HYPERSPE...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Daisuke Niina Naoto Yokoya Akira Iwasaki Department of Aeronautics and Astronautics the University of Tokyo Research Center for Advanced Science and Technology the University of Tokyo

ISBN: (纸本)9781479979301

Since SWIR hyperspectral data provides useful information on minerals and soils, the applications to geology, mining and agriculture are expected. In the SWIR region, hyperspectral sensors use mercury cadmium telluride detect responses to input radiances. Therefore, the pushbroom scanning of hyperspectral sensors causes stripe noises that disturb the minute spectral analysis. This work correct the artifact due to stripe noise at first using the conventional correction method, such as momentum matching. The remaining stripe patterns are corrected using a sparse coding technique, which is examined using real data.

关键词： Hyperspectral Calibration Denoise SWIR sparse coding

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Explicit versus implicit source estimation for blind multiple input single output system identification

Explicit versus implicit source estimation for blind multipl...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： A. J. Brockmeier J. C. Principe Electr. & Comput. Eng. Univ. of Florida Gainesville FL USA

ISBN: (纸本)9781467369985

sparsely-activated time series are found in many physical systems. In these cases, the signals can be approximated by convolution of sparse sources with a set of shift-invariant filters. When there is access to only one sensor, such that there is a single observation signal, identifying the source signals appears to be an ill-posed problem, but for very sparse sources it is still possible to learn the system. We discuss analysis techniques for sparsely activated signals, which retrieve sparse sources given the filters, and identify conditions when algorithms based on independent component analysis (ICA) and sparse coding can blindly estimate filters from a single noisy time-series. Many qualitative results have been made for learning shift-invariant bases on natural signals, but for a thorough understanding of the effect of sparsity, we quantitatively analyze results on synthetic examples, comparing how ICA and shift-invariant sparse coding approaches perform for multiple-source blind system identification.

关键词： ICA blind channel estimation dictionary learning sparse coding

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An Empirical Analysis of Different sparse Penalties for Autoencoder in Unsupervised Feature Learning

An Empirical Analysis of Different Sparse Penalties for Auto...

引用

International Joint Conference on Neural Networks

作者： Nan Jiang Wenge Rong Baolin Peng Yifan Nie Zhang Xiong School of Computer Science and Engineering Beihang University China

ISBN: (纸本)9781479919611

Machine learning algorithms depend heavily on the data representation, which dominates its success in experiment accuracy. Autoencoder model structure is proposed to learn from data a good representation with the least possible amount of distortion. Furthermore, it has been proven that boosting sparsity when learning representation can significantly improve performance on classification tasks and also make the feature vector easy to interpret. One straightforward approach for autoencoder to obtain sparse representation is to impose sparse penalty on its overall cost function. Nevertheless, few comparative analysis has been conducted to evaluate which sparse penalty term works better. In this paper, we adopt L1 norm, L2 norm, Student-t penalties, which are rarely deployed to penalise the hidden unit outputs, and commonly used penalty KL-divergence in the literature. Then, we present a detailed analysis to evaluate which penalty achieves better result in terms of reconstruction error, sparseness of representation and classification performance on test datasets. Experimental study on MNIST, CIFAR-10, SVHN, OPTDIGITS and NORB datasets reveals that all these penalties achieve sparse representation and outperforms representations learned by pure autoencoder on classification performance and sparseness of feature vectors. Moreover, we hope this topics and the practices would provide insights for future research.

关键词： autoencoder sparse coding sparse penalty unsupervised feature learning

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Mixture Noise Removal In Ultrasound Images Using SCoBeP and Low-rank Matrix Completion

Mixture Noise Removal In Ultrasound Images Using SCoBeP and ...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Nafise Barzigar Aminmohammad Roozgard Pramode Verma Samuel Cheng Department of Electrical and Computer Engineering Oklahoma University

ISBN: (纸本)9781457702150

Denoising as one of the most significant tools in ultrasound imaging was studied widely in the literature. However, most existing ultrasound image denoising algorithms have assumed the additive white Gaussian noise. In this work, we propose two efficient ultrasound image denoising methods that can handle a noise mixture of various types. Our methods are based on SCoBeP [1] and low-rank matrix completion as follows. In our first method, a noisy image is processed in blockwise manner and for each processed block we find candidate match pixels on other images using sparse coding and belief propagation, where in our second algorithm, we use overlapped patches to further lower the computation complexity. The blocks centered around these candidate pixels then will stack together and unreliable pixels will be removed using fast matrix completion method [2]. We demonstrate the effectiveness of our algorithm in removing the mixed noise through the results. We also compare with other denoising technique using matrix completion. Our methods results in comparable performance with significantly lower computation complexity.

关键词： Ultrasonography AWGN Completion Ultrasonic Waves Noisy Image Noise cancellation pixel matrices sparse coding Belief propagation Noise reduction

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AUTOMATIC IMAGE ANNOTATION USING INVERSE MAPS FROM SEMANTIC EMBEDDINGS

AUTOMATIC IMAGE ANNOTATION USING INVERSE MAPS FROM SEMANTIC ...

引用

IEEE International Conference on Image Processing

作者： J. J. Thiagarajan K. N. Ramamurthy P. Sattigeri P. T. Bremer A. Spanias Lawrence Livermore National Laboratory IBM Thomas J. Watson Research Center SenSIP Center School of ECEE Arizona State University

ISBN: (纸本)9781479957521

Human annotation in large scale image databases is time-consuming and error-prone. Since it is very hard to mine image databases using just visual features or textual descriptors, it is common to transform the image features into a semantically meaningful space. In this paper, we propose to perform image annotation in a semantic space inferred based on sparse representations. By constructing a semantic embedding for the visual features, that is constrained to be close to the tag embedding, we show that a robust inverse map can be used to predict the tags. Experiments using standard datasets show the effectiveness of the proposed approach in automatic image annotation when compared to existing methods.

关键词： Image annotation sparse coding Embedding Inverse map RBF interpolation

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Bayesian sparse Representation for Hyperspectral Image Super Resolution

Bayesian Sparse Representation for Hyperspectral Image Super...

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Naveed Akhtar Faisal Shafait Ajmal Mian The University of Western Australia

ISBN: (纸本)9781467369657

Despite the proven efficacy of hyperspectral imaging in many computer vision tasks, its widespread use is hindered by its low spatial resolution, resulting from hardware limitations. We propose a hyperspectral image super resolution approach that fuses a high resolution image with the low resolution hyperspectral image using non-parametric Bayesian sparse representation. The proposed approach first infers probability distributions for the material spectra in the scene and their proportions. The distributions are then used to compute sparse codes of the high resolution image. To that end, we propose a generic Bayesian sparse coding strategy to be used with Bayesian dictionaries learned with the Beta process. We theoretically analyze the proposed strategy for its accurate performance. The computed codes are used with the estimated scene spectra to construct the super resolution hyperspectral image. Exhaustive experiments on two public databases of ground based hyperspectral images and a remotely sensed image show that the proposed approach outperforms the existing state of the art.

关键词： super-resolution hyperspectral imagery High resolution imaging images state of the art Bayesian inference sparse representation sparse coding image resolution Probability distribution computer vision

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PARTIALLY TAGGED IMAGE CLUSTERING

PARTIALLY TAGGED IMAGE CLUSTERING

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IEEE International Conference on Image Processing

作者： Qiyue Yin Shu Wu Liang Wang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479983407

With the growth of tagged images, researchers are using this highly semantic tag information to assist some vision tasks such as image clustering. However, users may not tag some images at all or some of the images are partially annotated, and this will lead to performance degradation, which is rarely considered by previous works. To alleviate this problem, we propose a new model for image clustering assisted by partially observed tags. Our model enforces sparse representations obtained through sparse coding and latent tag representations learned via matrix factorization to be consistent with the partial image-tag observations. The partition of image database is finally performed using clustering algorithms (e.g., k-means) on the sparse representations. Extensive experiments demonstrate that the proposed model performs better than the state-of-the-art methods.

关键词： Image clustering Partially observed tag information Multi-view clustering sparse coding sparse coding images TAGGING Cluster Analysis visual databases matrix decomposition degradation of performance representations Imagery (Psychotherapy)

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