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检索条件"主题词=sparse coding"

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Stochastic Online Dictionary Learning for Speech Source Localization and Separation in Spherical Harmonic Domain

Stochastic Online Dictionary Learning for Speech Source Loca...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Vishnuvardhan Varanasi Rajesh Hegde Indian Institute of Technology Kanpur

ISBN: (纸本)9781538646595

Frequency and location dependent components in the speech signal can be decoupled by signal processing in the spherical harmonic domain. In this paper, a sparsity based method for joint source localization and separation method using online dictionary learning is proposed. Conventional sparsity based methods utilize an overcomplete dictionary to find a sparse linear combination of dictionary atoms. Online dictionary learning discussed herein, addresses the joint localization and separation problem by learning the dictionary atoms based on stochastic approximation. The location dependent terms present in the dictionary atoms at various frequencies are then clustered to find a robust estimate of number of sources and their locations. Using these estimates, the sources are separated from the mixture. Experiments on speech source localization and separation are conducted at various SNR. Performance evaluation scores like RMSE, log spectral distance and perceptual mean opinion scores indicate reasonable improvement over conventional methods for speech source separation.

关键词： Source Separation Source Localization Spherical Harmonics Online Dictionary Learning sparse coding Position measurement Source separation sparse coding spherical harmonic analysis Dictionaries as Topic Voice Signal separation

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sparse CONCEPT DISCRIMINANT MATRIX FACTORIZATION FOR IMAGE REPRESENTATION

SPARSE CONCEPT DISCRIMINANT MATRIX FACTORIZATION FOR IMAGE R...

引用

IEEE International Conference on Image Processing

作者： Meng Pang Chuang Lin Risheng Liu Xin Fan Jifeng Jiang Zhongxuan Luo School of Software Dalian University of Technology

ISBN: (纸本)9781479983407

Over the past few decades, matrix factorization has attracted considerable attention for image representation. It is desired for a matrix factorization technique to find the basis that is able to capture highly discriminant information as well as to preserve the intrinsic manifold structure. Besides, the basis has to generate a sparse representation for a given image. In this paper, we propose a matrix factorization method called sparse concept Discriminant Matrix Factorization (SDMF) by combining a novel fisher-like criterion with the sparse coding. The criterion is discriminant enough across different feature spaces, and meanwhile maintains locally neighboring structures. The proposed method is general for both cases with and without class labels, hence yielding supervised and un-supervised SDMFs. Experimental results show that SDMF provides better representation with higher performance on two tasks (image recognition and clustering) compared with the existing matrix factorization methods.

关键词： sparse coding Fisher-like criterion Matrix factorization Manifold learning Image representation matrix decomposition image representation sparse coding Discriminant Eigenvector images factorization

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sparse Image Representation with Epitomes

Sparse Image Representation with Epitomes

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Louise Benoit Julien Mairal Francis Bach Jean Ponce Ecole Normale Superieure INRIA

ISBN: (纸本)9781457703942

sparse coding, which is the decomposition of a vector using only a few basis elements, is widely used in machine learning and image processing. The basis set, also called dictionary, is learned to adapt to specific data. This approach has proven to be very effective in many image processing tasks. Traditionally, the dictionary is an unstructured "flat" set of atoms. In this paper, we study structured dictionaries which are obtained from an epitome, or a set of epitomes. The epitome is itself a small image, and the atoms are all the patches of a chosen size inside this image. This considerably reduces the number of parameters to learn and provides sparse image decompositions with shift-invariance properties. We propose a new formulation and an algorithm for learning the structured dictionaries associated with epitomes, and illustrate their use in image de-noising tasks.

关键词： Dictionaries as Topic image representation Imagery (Psychotherapy) images Atoms sparse coding Basis image denoising Image image processing task Set (Psychology)

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Discriminatively Trained sparse Code Gradients for Contour Detection 12

Discriminatively Trained Sparse Code Gradients for Contour D...

引用

Annual Conference on Neural Information Processing Systems

作者： Xiaofeng Ren Liefeng Bo Intel Science and Technology Center for Pervasive Computing Intel Labs Seattle WA 98195 USA

ISBN: (纸本)9781627480031

Finding contours in natural images is a fundamental problem that serves as the basis of many tasks such as image segmentation and object recognition. At the core of contour detection technologies are a set of hand-designed gradient features, used by most approaches including the state-of-the-art Global Pb (gPb) operator. In this work, we show that contour detection accuracy can be significantly improved by computing sparse Code Gradients (SCG), which measure contrast using patch representations automatically learned through sparse coding. We use K-SVD for dictionary learning and Orthogonal Matching Pursuit for computing sparse codes on oriented local neighborhoods, and apply multi-scale pooling and power transforms before classifying them with linear SVMs. By extracting rich representations from pixels and avoiding collapsing them prematurely, sparse Code Gradients effectively learn how to measure local contrasts and find contours. We improve the F-measure metric on the BSDS500 benchmark to 0.74 (up from 0.71 of gPb contours). Moreover, our learning approach can easily adapt to novel sensor data such as Kinect-style RGB-D cameras: sparse Code Gradients on depth maps and surface normals lead to promising contour detection using depth and depth+color, as verified on the NYU Depth Dataset.

关键词： contour detection Outlines contour Codes surface normal sparse coding matching pursuits Sensor Data object recognition image segmentation pixel

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sparse modeling for topic-oriented video summarization

Sparse modeling for topic-oriented video summarization

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rameswar Panda Amit K. Roy-Chowdhury Electrical and Computer Engineering Department University of California Riverside United States of America

ISBN: (纸本)9781509041183

While most existing video summarization approaches aim to extract an informative summary of a single video, we propose an unsupervised framework for summarizing topic-related videos by exploring complementarity within videos. We develop a novel sparse optimization method to extract a diverse summary that is both interesting and representative in describing the video collection. To efficiently solve our optimization problem, we develop an alternating minimization algorithm that minimizes the overall objective function with respect to one video at a time while fixing the other videos. Experimental results demonstrate that our approach clearly outperforms the state-of-the-art methods.

关键词： Video Summarization sparse coding Videotapes sparse coding optimization method Objective function

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Image Denoising and Inpainting with Deep Neural Networks 12

Image Denoising and Inpainting with Deep Neural Networks

引用

Annual Conference on Neural Information Processing Systems

作者： Junyuan Xie Linli Xu Enhong Chen School of Computer Science and Technology University of Science and Technology of China

ISBN: (纸本)9781627480031

We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blind inpainting. Our method's performance in the image denoising task is comparable to that of KSVD which is a widely used sparse coding technique. More importantly, in blind image inpainting task, the proposed method provides solutions to some complex problems that have not been tackled before. Specifically, we can automatically remove complex patterns like superimposed text from an image, rather than simple patterns like pixels missing at random. Moreover, the proposed method does not need the information regarding the region that requires inpainting to be given a priori. Experimental results demonstrate the effectiveness of the proposed method in the tasks of image denoising and blind inpainting. We also show that our new training scheme for DA is more effective and can improve the performance of unsupervised feature learning.

关键词： image denoising inpaint sparse coding DOE DESIGN AGENCY designer's alterations DA Religious Missions Blind Imagery (Psychotherapy) images pixel

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LEARNING OVERCOMPLETE SPARSIFYING TRANSFORMS FOR SIGNAL PROCESSING

LEARNING OVERCOMPLETE SPARSIFYING TRANSFORMS FOR SIGNAL PROC...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Saiprasad Ravishankar Yoram Bresler Department of Electrical and Computer Engineering and the Coordinated Science Laboratory University of Illinois Urbana-Champaign IL 61801 USA

ISBN: (纸本)9781479903573

Adaptive sparse representations have been very popular in numerous applications in recent years. The learning of synthesis sparsifying dictionaries has particularly received much attention, and such adaptive dictionaries have been shown to be useful in applications such as image denoising, and magnetic resonance image reconstruction. In this work, we focus on the alternative sparsifying transform model, for which sparse coding is cheap and exact, and study the learning of tall or overcomplete sparsifying transforms from data. We propose various penalties that control the sparsifying ability, condition number, and incoherence of the learnt transforms. Our alternating algorithm for transform learning converges empirically, and significantly improves the quality of the learnt transform over the iterations. We present examples demonstrating the promising performance of adaptive overcomplete transforms over adaptive overcomplete synthesis dictionaries learnt using K-SVD, in the application of image denoising.

关键词： Sparsifying transform learning sparse representations dictionary learning Overcomplete representations signal processing Transform sparse representation Dictionaries as Topic image denoising Learning incoherence Self tuning sparse coding penalty iterative methods

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Dimensionality Reduction Using the sparse Linear Model 11

Dimensionality Reduction Using the Sparse Linear Model

引用

Annual Conference on Neural Information Processing Systems

作者： Ioannis Gkioulekas Todd Zickler Harvard SEAS Cambridge MA 02138

ISBN: (纸本)9781618395993

We propose an approach for linear unsupervised dimensionality reduction, based on the sparse linear model that has been used to probabilistically interpret sparse coding. We formulate an optimization problem for learning a linear projection from the original signal domain to a lower-dimensional one in a way that approximately preserves, in expectation, pairwise inner products in the sparse domain. We derive solutions to the problem, present nonlinear extensions, and discuss relations to compressed sensing. Our experiments using facial images, texture patches, and images of object categories suggest that the approach can improve our ability to recover meaningful structure in many classes of signals.

关键词： dimensionality reduction linear models sparse coding Compressed sensing PROBLEM SOLUTION original signal Mug shots signals

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SEMANTICS CONSTRAINED DICTIONARY LEARNING FOR SIGNER-INDEPENDENT SIGN LANGUAGE RECOGNITION

SEMANTICS CONSTRAINED DICTIONARY LEARNING FOR SIGNER-INDEPEN...

引用

IEEE International Conference on Image Processing

作者： Fang Yin Xiujuan Chai Yu Zhou Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Institute of Information Engineering Chinese Academy of Sciences

ISBN: (纸本)9781479983407

In this paper, a sparse coding based framework is proposed for sign language recognition (SLR), especially for the signer-independent case. To deal with the inter-signer variation, a dictionary capturing the common features among different signers is learnt by considering the semantic constraint. Thus for a given sign from an unknown signer, the sparse representation, which maintains more information of this specific sign class while neglecting the identity information as much as possible, can be generated. In our implementation, each sign is partitioned into a fixed number of fragments and the features fusing hand shape and moving trajectory are extracted from the fragments. The dictionary learnt from the training fragments can be taken as the basic subunits of signs and each fragment of sign video can be coded by these basis vectors. Finally, the recognition result is achieved through SVM with the concatenated sparse coding features of the fragments. The experiments and comparisons show that our method is more effective for the signer-independent recognition problem than other baseline methods. At the same time, it also performs well for the signer-dependent case.

关键词： sparse coding Dictionary learning Sign language recognition Signer-independent Semantic constraint

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DUAL-LAYER BAG-OF-FRAMES MODEL FOR MUSIC GENRE CLASSIFICATION

DUAL-LAYER BAG-OF-FRAMES MODEL FOR MUSIC GENRE CLASSIFICATIO...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Chin-Chia Michael Yeh Li Su Yi-Hsuan Yang Research Center for Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)9781479903573

This paper concerns the development of a music dictionary-based model for summarizing local feature descriptors computed over time. Comparing to a holistic representation, this text-like, bag-of-frames representation better captures the rich and time -varying information of music. However, the dictionary used in classical bag-of-frames model only captures frame-level elements of the music;thus, there exists a semantic gap between the dictionary element and commonly seen music description. In order to reduce the gap, a new feature representation called dual-layer bag-of-frames is proposed in this paper. It models the music with a two layer structure, where the first-layer dictionary captures the frame-level characteristics, and the second-layer dictionary captures the segment-level semantics. This hierarchical structure resembles the alphabet-word-document structure of text. Our result demonstrates that the proposed dual-layer bag-of-frames feature achieves state-of-the-art accuracy of music genre classification. The classification accuracy for the GTZAN benchmark reaches 86.7% with dictionary trained from GTZAN, and 83.6% with dictionary trained from another data set USPOP.

关键词： sparse coding Deep structure Audio alphabets Audio words Music genre classification

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