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Using unlabeled data in a sparse-coding framework for human activity recognition

PERVASIVE AND MOBILE COMPUTING 2014年 15卷 242-262页

作者： Bhattacharya, Sourav Nurmi, Petteri Hammerla, Nils Poeltz, Thomas Univ Helsinki Dept Comp Sci HIIT FIN-00014 Helsinki Finland Newcastle Univ Sch Comp Sci Culture Lab Newcastle Upon Tyne NE1 7RU Tyne & Wear England

We propose a sparse-coding framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches. (i) It automatically derives a compact, sparse and meaningful feature representation of sensor data that does not rely on prior expert knowledge and generalizes well across domain boundaries. (ii) It exploits unlabeled sample data for bootstrapping effective activity recognizers, i.e., substantially reduces the amount of ground truth annotation required for model estimation. Such unlabeled data is easy to obtain, e. g., through contemporary smartphones carried by users as they go about their everyday activities. Based on the self-taught learning paradigm we automatically derive an over-complete set of basis vectors from unlabeled data that captures inherent patterns present within activity data. Through projecting raw sensor data onto the feature space defined by such over-complete sets of basis vectors effective feature extraction is pursued. Given these learned feature representations, classification backends are then trained using small amounts of labeled training data. We study the new approach in detail using two datasets which differ in terms of the recognition tasks and sensor modalities. Primarily we focus on a transportation mode analysis task, a popular task in mobile-phone based sensing. The sparse-coding framework demonstrates better performance than the state-of-the-art in supervised learning approaches. More importantly, we show the practical potential of the new approach by successfully evaluating its generalization capabilities across both domain and sensor modalities by considering the popular Opportunity dataset. Our feature learning approach outperforms state-of-the-art approaches to analyzing activities of daily living. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Activity recognition sparse-coding Machine learning Unsupervised learning

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sparse-coding ADAPTED TO SAR IMAGES WITH AN APPLICATION TO DESPECKLING 38

SPARSE-CODING ADAPTED TO SAR IMAGES WITH AN APPLICATION TO D...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Tabti, Sonia Verdoliva, Luisa Poggi, Giovanni Normandie Univ ENSICAEN CNRS GREYC 6 Bd Marchal Juin F-14000 Caen France Univ Federico II Napoli DIETI Via Claudio 21 I-80125 Naples Italy

ISBN: (纸本)9781538671504

In this paper, we propose a sparsity-based despeckling approach. The first main contribution of this work is the elaboration of a sparse-coding algorithm adapted to the statistics of SAR images. In fact, most sparse-coding algorithms for SAR data apply a logarithmic transform to data, so as to convert the noise from multiplicative to additive. Then, a Gaussian prior is adopted. However, using a more suitable prior for SAR data avoids introducing artifacts. The second main contribution proposed is to predict the optimal sparsity degree for each patch based on local image features. Experiments show that this strategy improves upon traditional sparse coding with a low-error-rate stopping criterion.

关键词： Patches sparse-coding coefficient of variation despeckling SAR images

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Empirical Transition Probability Indexing sparse-coding Belief Propagation (ETPI-SCoBeP) Genome Sequence Alignment

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CANCER INFORMATICS 2014年第Sup1期13卷 159-165页

作者： Roozgard, Aminmohammad Barzigar, Nafise Wang, Shuang Jiang, Xiaoqian Cheng, Samuel Univ Oklahoma Sch Elect & Comp Engn Tulsa OK 73019 USA Univ Calif San Diego Div Biomed Informat San Diego CA 92093 USA

The advance in human genome sequencing technology has significantly reduced the cost of data generation and overwhelms the computing capability of sequence analysis. Efficiency, efficacy, and scalability remain challenging in sequence alignment, which is an important and foundational operation for genome data analysis. In this paper, we propose a two-stage approach to tackle this problem. In the preprocessing step, we match blocks of reference and target sequences based on the similarities between their empirical transition probability distributions using belief propagation. We then conduct a refined match using our recently published sparse-coding belief propagation (SCoBeP) technique. Our experimental results demonstrated robustness in nucleotide sequence alignment, and our results are competitive to those of the SOAP aligner and the BWA algorithm. Moreover, compared to SCoBeP alignment, the proposed technique can handle sequences of much longer lengths.

关键词： empirical transition probability indexing sparse-coding belief propagation genome sequence alignment

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Application of sparse-coding Super-Resolution to 16-Bit DICOM Images for Improving the Image Resolution in MRI

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Open Journal of Medical Imaging 2017年第4期7卷 144-155页

作者： Junko Ota Kensuke Umehara Naoki Ishimaru Takayuki Ishida Department of Medical Physics and Engineering Graduate School of Medicine Osaka University Suita Japan

Purpose: To improve the image resolution of magnetic resonance imaging (MRI), conventional interpolation methods are commonly used to magnify images via various image processing approaches;however, these methods tend to produce artifacts. While super-resolution (SR) schemes have been introduced as an alternative approach to apply medical imaging, previous studies applied SR only to medical images in 8-bit image format. This study aimed to evaluate the effectiveness of sparse-coding super-resolution (ScSR) for improving the image quality of reconstructed high-resolution MR images in 16-bit digital imaging and communications in medicine (DICOM) image format. Materials and Methods: Fifty-nine T1-weighted images (T1), 84 T2-weighted images (T2), 85 fluid attenuated inversion recovery (FLAIR) images, and 30 diffusion-weighted images (DWI) were sampled from The Repository of Molecular Brain Neoplasia Data as testing datasets, and 1307 non-medical images were sampled from the McGill Calibrated Color Image Database as a training dataset. We first trained the ScSR to prepare dictionaries, in which the relationship between low- and high-resolution images was learned. Using these dictionaries, a high-resolution image was reconstructed from a 16-bit DICOM low-resolution image downscaled from the original test image. We compared the image quality of ScSR and 4 interpolation methods (nearest neighbor, bilinear, bicubic, and Lanczos interpolations). For quantitative evaluation, we measured the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Results: The PSNRs and SSIMs for the ScSR were significantly higher than those of the interpolation methods for all 4 MRI sequences (PSNR: p p Conclusion: ScSR provides significantly higher image quality in terms of enhancing the resolution of MR images (T1, T2, FLAIR, and DWI) in 16-bit DICOM format compared to the interpolation methods.

关键词： Super-Resolution sparse-coding Super-Resolution (ScSR) MRI DICOM 16-Bit

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Performance Evaluation of Super-Resolution Methods Using Deep-Learning and sparse-coding for Improving the Image Quality of Magnified Images in Chest Radiographs

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Open Journal of Medical Imaging 2017年第3期7卷 100-111页

作者： Kensuke Umehara Junko Ota Naoki Ishimaru Shunsuke Ohno Kentaro Okamoto Takanori Suzuki Takayuki Ishida Department of Medical Physics and Engineering Graduate School of Medicine Osaka University Suita Japan

Purpose: To detect small diagnostic signals such as lung nodules in chest radiographs, radiologists magnify a region-of-interest using linear interpolation methods. However, such methods tend to generate over-smoothed images with artifacts that can make interpretation difficult. The purpose of this study was to investigate the effectiveness of super-resolution methods for improving the image quality of magnified chest radiographs. Materials and Methods: A total of 247 chest X-rays were sampled from the JSRT database, then divided into 93 training cases with non-nodules and 154 test cases with lung nodules. We first trained two types of super-resolution methods, sparse-coding super-resolution (ScSR) and super-resolution convolutional neural network (SRCNN). With the trained super-resolution methods, the high-resolution image was then reconstructed using the super-resolution methods from a low-resolution image that was down-sampled from the original test image. We compared the image quality of the super-resolution methods and the linear interpolations (nearest neighbor and bilinear interpolations). For quantitative evaluation, we measured two image quality metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). For comparative evaluation of the super-resolution methods, we measured the computation time per image. Results: The PSNRs and SSIMs for the ScSR and the SRCNN schemes were significantly higher than those of the linear interpolation methods (p p p Conclusion: Super-resolution methods provide significantly better image quality than linear interpolation methods for magnified chest radiograph images. Of the two tested schemes, the SRCNN scheme processed the images fastest;thus, SRCNN could be clinically superior for processing radiographs in terms of both image quality and processing speed.

关键词： Deep Learning Super-Resolution Super-Resolution Convolutional Neural Network (SRCNN) sparse-coding Super-Resolution (ScSR) Chest X-Ray

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Image super-resolution using sparse coding over redundant dictionary based on effective image representations

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年 26卷 50-65页

作者： Sajjad, Muhammad Mehmood, Irfan Baik, Sung Wook Sejong Univ Coll Elect & Informat Engn Seoul South Korea

Recent years have shown a growing research interest in the sparse-representation of signals. Signals are described through sparse linear combinations of signal-atoms over a redundant-dictionary. Therefore, we propose a novel super-resolution framework using an overcomplete-dictionary based on effective imagerepresentations such as edges, contours and high-order structures. This scheme recovers the vector of common sparse-representations between low-resolution and corresponding high-resolution imagepatches by solving the l(1)-regularized least-squared problem;subsequently, it reconstructs the HR output by multiplying it with the learned dictionary. The dictionary used in the proposed-technique contains more effective image-representations than those in previous approaches because it contains featuredescriptors such as edges, contours and motion-selective features. Therefore, the proposed-technique is more robust to various types of distortion. A saliency-map quickens this technique by confining the optimization-process to visually salient regions. Experimental analyses confirm the effectiveness of the proposed-scheme, and its quantitative and qualitative performance as compared with other state-of-the-art super-resolution algorithms. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Super-resolution Reconstruction Denoising Representation-coefficients sparse-coding vercomplete-dictionary Visual-saliency Pursuit algorithm

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Face recognition using localized features based on non-negative sparse coding

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MACHINE VISION AND APPLICATIONS 2007年第2期18卷 107-122页

作者： Shastri, Bhavin J. Levine, Martin D. McGill Univ Ctr Intelligent Machines Montreal PQ H3A 2A7 Canada McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 2A7 Canada

Neural networks in the visual system may be performing sparse coding of learnt local features that are qualitatively very similar to the receptive fields of simple cells in the primary visual cortex, V1. In conventional sparse coding, the data are described as a combination of elementary features involving both additive and subtractive components. However, the fact that features can 'cancel each other out' using subtraction is contrary to the intuitive notion of combining parts to form a whole. Thus, it has recently been argued forcefully for completely non-negative representations. This paper presents Non-Negative sparse coding (NNSC) applied to the learning of facial features for face recognition and a comparison is made with the other part-based techniques, Non-negative Matrix Factorization (NMF) and Local-Non-negative Matrix Factorization (LNMF). The NNSC approach has been tested on the Aleix-Robert (AR), the Face Recognition Technology (FERET), the Yale B, and the Cambridge ORL databases, respectively. In doing so, we have compared and evaluated the proposed NNSC face recognition technique under varying expressions, varying illumination, occlusion with sunglasses, occlusion with scarf, and varying pose. Tests were performed with different distance metrics such as the L-1-metric, L-2-metric, and Normalized Cross-Correlation (NCC). All these experiments involved a large range of basis dimensions. In general, NNSC was found to be the best approach of the three part-based methods, although it must be observed that the best distance measure was not consistent for the different experiments.

关键词： face recognition sparse-coding non-negative representations distance metrics

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Resolution-invariant coding for continuous image super-resolution

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NEUROCOMPUTING 2012年 82卷 21-28页

作者： Wang, Jinjun Zhu, Shenghuo Epson Res & Dev Inc 2580 Orchard Pkwy San Jose CA 95131 USA NEC Labs Amer Inc Cupertino CA 95014 USA

The paper presents the resolution-invariant image representation ((sic)IIR) framework. It applies sparse-coding with multi-resolution codebook to learn resolution-invariant sparse representations of local patches. An input image can be reconstructed to higher resolution at not only discrete integer scales, as that in many existing super-resolution works, but also continuous scales, which functions similar to 2-D image interpolation. The (sic)IIR framework includes the methods of building a multi-resolution bases set from training images, learning the optimal sparse resolution-invariant representation of an image, and reconstructing the missing high-frequency information at continuous resolution level. Both theoretical and experimental validations of the resolution invariance property are presented in the paper. Objective comparison and subjective evaluation show that the RN framework based image resolution enhancement method outperforms existing methods in various aspects. (c) 2011 Elsevier B.V. All rights reserved.

关键词： Image representation sparse-coding Image super-resolution

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Resolution enhancement based on learning the sparse association of image patches

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PATTERN RECOGNITION LETTERS 2010年第1期31卷 1-10页

作者： Wang, Jinjun Zhu, Shenghuo Gong, Yihong NEC Labs Amer Inc Cupertino CA 95014 USA

Example-based image super-resolution techniques model the co-occurrence patterns between the middle and high frequency layers of example images to estimate the missing high frequency component for low resolution input. However, many existing approaches seek to estimate the optimal solution within a small set of candidates by using empirical criteria. Hence their representational performance is limited by the quality of the candidate set, and the generated super-resolution image is unstable, with noticeable artifacts. In this paper, we propose a novel image super-resolution method based on learning the sparse association between input image patches and the example image patches. We improve an existing sparse-coding algorithm to find sparse association between image patches. We also propose an iterative training strategy to learn a redundancy reduced basis set to speed up the super-resolution process. Comparing to existing example-based approaches, the proposed method significantly improves image quality, and the produced super-resolution images are sharp and natural, with no obvious artifact. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Image super-resolution Image representation sparse-coding

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Learning sequences of sparse correlated patterns using small-world attractor neural networks: An application to traffic videos

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NEUROCOMPUTING 2011年第14-15期74卷 2361-2367页

作者： Gonzalez, Mario Dominguez, David Sanchez, Angel Univ Autonoma Madrid EPS E-28049 Madrid Spain Univ Rey Juan Carlos DCC ETSII Madrid 28933 Spain

The goal of this work is to learn and retrieve a sequence of highly correlated patterns using a Hopfield-type of attractor neural network (ANN) with a small-world connectivity distribution. For this model, we propose a weight learning heuristic which combines the pseudo-inverse approach with a row-shifting schema. The influence of the ratio of random connectivity on retrieval quality and learning time has been studied. Our approach has been successfully tested on a complex pattern, as it is the case of traffic video sequences, for different combinations of the involved parameters. Moreover, it has demonstrated to be robust with respect to highly variable frame activity. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Attractor network Small-world sparse-coding Correlated patterns Temporal sequence Video retrieval Traffic analysis

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