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检索条件"主题词=sparse coding"

共 2101 条记录，以下是1991-2000 订阅

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Visual Saliency Detection Based on Deredundancy and Global Contrast

Visual Saliency Detection Based on Deredundancy and Global C...

引用

The 2nd International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation(CDMMS 2012)

作者： Huan Shen Xiaodong Miao Yunsheng Tan College of Energy & Power Engineering Nanjing University of Aeronautics and Astronautics

ISBN: (纸本)9783037855409

A new method for saliency detection is *** on the sparse coding model,we propose a power spectral filter to eliminate the second-order residual correlation,which suppress the global repeated items *** addition,aim to modeling the mechanism of the human retina prior response to high-contrast stimuli,the effect of color context is *** result indicates that our method has high-quality detection performance with respect to the ability not only to highlight the salient objects in complex environment but also to pop up them uniformly.

关键词： Image Processing Machine Vision Visual Attention sparse coding

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COLOR DE-RENDERING USING COUPLED DICTIONARY LEARNING

COLOR DE-RENDERING USING COUPLED DICTIONARY LEARNING

引用

IEEE International Conference on Image Processing

作者： Muhammad Rushdi Mohsen Ali Jeffrey Ho Computer and Information Science and Engineering University of Florida

ISBN: (纸本)9781479923427

Consumer-level digital cameras typically post-process raw captured image data to produce enhanced visually appealing output RGB images. Post-processing operations include color gamut compression, tone mapping and other non-linear color corrections. However, raw image data is needed for many computer vision applications such as photometric stereo, shape from shading, and color constancy. Recovering raw image data from RGB images is complicated by the high non-linearity of the post-processing operations. In this paper, we propose a coupled dictionary scheme to model the relationship between the raw and RGB color image spaces of consumer cameras. Dictionary learning is regularized by sparsity constraints on feature representation. As well, we explore a more elaborate variant of coupled dictionary schemes that models the feature coupling more accurately. We test the proposed dictionary learning schemes on many commercial camera datasets. Our experimental results show accurate recovery of raw image data that looks visually indistinguishable from the ground truth.

关键词： Coupled features Dictionary learning Color de-rendering Color transformation sparse coding image data sparse coding color conversion immatures Dictionaries as Topic red-green-blue colour model images Color POST-PROCESSING nonlinearity ground truth

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sparse Spatio-Temporal Representation of Joint Shape-Motion Cues for Human Action Recognition in Depth Sequences

Sparse Spatio-Temporal Representation of Joint Shape-Motion ...

引用

IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future

作者： Quang D. Tran Ngoc Q. Ly Faculty of Information Technology University of Science

ISBN: (纸本)9781479913480

The availability of 3D sensors has recently made it possible to capture depth maps in real time, which simplifies a variety of visual recognition tasks, including object/action classification, 3D reconstruction, etc. We address here the problems of human action recognition in depth sequences. On one hand, we present a new joint shape-motion descriptor which we call as 3D Spherical Histogram of Oriented Normal Vectors (3DS-HONV), since it is a spatio-temporal extension of the original HONV quantized in a 3D spherical coordinate. We further prove that the Optical Flow fields in depth sequences could be used in conjunction with the presented descriptor to augment the ability of capturing in-plane movements;the experiments later show that this combination is more efficient than the standalone 3DSHONV. In addition, a discriminative dictionary learning and feature representation via sparse coding is applied to proposed descriptors to relieve the intrinsic effects of noise and capture high-level patterns. By learning these sparse and distinctive representations, we demonstrate large improvements over the state-of-the-art on two challenging benchmarks, which results with an overall accuracy of 91.92% on the MSRAction3D and 93.31% on the MSRGesture3D datasets, respectively.

关键词： sparse coding Noise effects Recognition (Psychology) Facial Recognition Descriptors Joints Joints Spherical coordinates

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SPARSITY CONSTRAINED IMAGE RECONSTRUCTION USING NONLINEAR DICTIONARY ATOMS WITH TIME-SHIFTED OMP SIGNAL coding ALGORITHM

SPARSITY CONSTRAINED IMAGE RECONSTRUCTION USING NONLINEAR DI...

引用

IEEE Canadian Conference on Electrical and Computer Engineering

作者： Mathiruban Tharmalingam Kaamran Raahemifar Department of Electrical Engineering & Computer Eng. Ryerson University

ISBN: (纸本)9781479900312

Complex signals such as images, audio and video recordings can be represented by a large over-complete dictionary without significant compromise on the representation quality. An over-complete dictionary has many more columns than the number of rows. Large over-complete dictionaries can produce sparse representation vectors and provide significant improvements in the reconstructed signal quality because it contains many patterns to select from. The use of the over-complete dictionaries and sparse coding has been successfully applied in compression, de-noising, and pattern recognition applications within the last few decades. An example of an over-complete dictionary that has seen a great deal of success in image processing applications is the Discrete Cosine Transform (DCT) dictionary. However, we propose a novel non-linear over-complete dictionary that improves the quality of the signal representation while reducing the number of non-zero elements to represent the signal. The proposed non-linear dictionary has demonstrated through experimental results to be superior to the DCT dictionary by achieving higher signal to noise ratio (SNR) in the reconstructed images.

关键词： sparse coding Non-linear dictionaries atoms DCT Time-shifted OMP Image reconstruction

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Riemannian coding and Dictionary Learning: Kernels to the Rescue

Riemannian Coding and Dictionary Learning: Kernels to the Re...

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Mehrtash Harandi Mathieu Salzmann Australian National University & NICTA

ISBN: (纸本)9781467369657

While sparse coding on non-flat Riemannian manifolds has recently become increasingly popular, existing solutions either are dedicated to specific manifolds, or rely on optimization problems that are difficult to solve, especially when it comes to dictionary learning. In this paper, we propose to make use of kernels to perform coding and dictionary learning on Riemannian manifolds. To this end, we introduce a general Riemannian coding framework with its kernel-based counterpart. This lets us (i) generalize beyond the special case of sparse coding;(ii) introduce efficient solutions to two coding schemes;(iii) learn the kernel parameters;(iv) perform unsupervised and supervised dictionary learning in a much simpler manner than previous Riemannian coding methods. We demonstrate the effectiveness of our approach on three different types of non-flat manifolds, and illustrate its generality by applying it to Euclidean spaces, which also are Riemannian manifolds.

关键词： Dictionaries as Topic Rescue Manifolds sparse coding Seeds Kernel coding

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SCALABLE BAYESIAN NONPARAMETRIC DICTIONARY LEARNING

SCALABLE BAYESIAN NONPARAMETRIC DICTIONARY LEARNING

引用

European Signal Processing Conference

作者： Sarper Sertoglu John Paisley Department of Computer Science Columbia University Department of Electrical Engineering Columbia University

ISBN: (纸本)9781479988518

We derive a stochastic EM algorithm for scalable dictionary learning with the beta-Bernoulli process, a Bayesian nonparametric prior that learns the dictionary size in addition to the sparse coding of each signal. The core EM algorithm provides a new way for doing inference in nonparametric dictionary learning models and has a close similarity to other sparse coding methods such as K-SVD. Our stochastic extension for handling large data sets is closely related to stochastic variational inference, with the stochastic update for one parameter exactly that found using SVI. We show our algorithm compares well with K-SVD and total variation minimization on a denoising problem using several images.

关键词： expectation-maximisation algorithm sparse coding Dictionaries as Topic one-parameter Stochastic Sludge volume index Learning Dataset Variational Inference

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A low-rank recovery dictionary learning method for spectrum data denoising

A low-rank recovery dictionary learning method for spectrum ...

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第33届中国过程控制会议

作者： Jiayi Yue Jingxuan Geng Yonggang Li Lijuan Lan Jie Han Chunhua Yang College of Automation Central South University

Spectral analysis is widely used in various detection activities,unfortunately,the detection accuracy is greatly reduced due to the interference of multiple noises in the spectrum data acquisition *** order to remove the mixed noise in the spectrum data and improve the detection accuracy,we propose a low-rank recovery dictionary learning ***,the method considers the effects of multiple noises of Gaussian noise and sparse noise,and establishes a dictionary learning model that can reduce these ***,the corresponding optimization method is designed to solve the ***,relevant experiments show the feasibility and superiority of the proposed spectrum data denoising method.

关键词： dictionary learning spectrum denoising sparse coding mixed noise

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An Adaptive Orthogonal Matching Pursuit Algorithm Based on Redundancy Dictionary

An Adaptive Orthogonal Matching Pursuit Algorithm Based on R...

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International Conference on Fuzzy Systems and Knowledge Discovery

作者： Yumin Tian Zhihui Wang School of Computer Science and Technology Xidian University

ISBN: (纸本)9781467352512

The reconstruction performance of an orthogonal matching pursuit algorithm is poor due to less observation values. An observation matrix design method which can adaptively ensure the sample size based on the image information is proposed. To make the algorithm more sparsely representative, an adaptive orthogonal matching pursuit algorithm based on the redundant dictionary is discussed by using a K-SVD dictionary training method to get a sparse dictionary. Experimental results show that the algorithm not only solves the problem that the sample size is small, but also improves the image reconstruction quality.

关键词： Orthogonal matching pursuit Redundancy dictionary sparse coding

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HARMONIC VARIABLE-SIZE DICTIONARY LEARNING FOR MUSIC SOURCE SEPARATION

HARMONIC VARIABLE-SIZE DICTIONARY LEARNING FOR MUSIC SOURCE ...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Steven K. Tjoa Matthew C. Stamm W. Sabrina Lin K. J. Ray Liu Signals and Information Group Dept. of Electrical and Computer Engineering University of Maryland - College Park MD 20742 USA

ISBN: (纸本)9781424442959;9781424442966

Dictionary learning through matrix factorization has become widely popular for performing music transcription and source separation. These methods learn a concise set of dictionary atoms which represent spectrograms of musical objects. However, there is no guarantee that the atoms learned will be perceptually meaningful, particularly when there exists significant spectral and temporal overlap among the musical sources. In this paper, we propose a novel dictionary learning method that imposes additional harmonic constraints upon the atoms of the learned dictionary while allowing the dictionary size to grow appropriately during the learning procedure. When there is significant spectral-temporal overlap among the musical sources, our method outperforms popular existing matrix factorization methods as measured by the recall and precision of learned dictionary atoms.

关键词： Nonnegative matrix factorization pitch estimation sparse coding music transcription Pitch estimation matrix decomposition sparse coding Dictionaries as Topic Atoms harmonics Spectrogram Source separation Multiple signal classification

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Structure-aware classification using supervised dictionary learning

Structure-aware classification using supervised dictionary l...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yael Yankelevsky Michael Elad Computer Science Department Technion - Israel Institute of Technology Haifa 32000 Israel

ISBN: (纸本)9781509041183

In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the observations. A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed from the training data or learned and adapted along the dictionary learning process. The combination of these two terms promotes the discriminative power of the learned sparse representations and leads to improved classification accuracy. The proposed method was evaluated on several different datasets, representing both single-label and multi-label classification problems, and demonstrated better performance compared with other dictionary based approaches.

关键词： supervised dictionary learning sparse coding graph Laplacian classification

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