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检索条件"主题词=sparse coding"

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ADAPTIVE COMPRESSED SENSING - A NEW CLASS OF SELF-ORGANIZING coding MODELS FOR NEUROSCIENCE

ADAPTIVE COMPRESSED SENSING - A NEW CLASS OF SELF-ORGANIZING...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： William K. Coulter Christopher J. Hillar Guy Isley Friedrich T. Sommer University of California Berkeley 156 Stanley Hall MC 3220 Berkeley CA 94720-3220 Mathematical Sciences Research Institute 17 Gauss Way Berkeley CA 94120

ISBN: (纸本)9781424442959

sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex [1]. However, conventional sparse coding models require that the coding circuit can fully sample the sensory data in a one-to-one fashion, a requirement not supported by experimental data from the thalamo-cortical projection. To relieve these strict wiring requirements, we propose a sparse coding network constructed by introducing synaptic learning in the framework of compressed sensing. We demonstrate a new model that evolves biologically realistic, spatially smooth receptive fields despite the fact that the feedforward connectivity subsamples the input and thus the learning must rely on an impoverished and distorted account of the original visual data. Further, we demonstrate that the model could form a general scheme of cortical communication: it can form meaningful representations in a secondary sensory area, which receives input from the primary sensory area through a "compressing" cortico-cortical projection. Finally, we prove that our model belongs to a new class of sparse coding algorithms in which recurrent connections are essential in forming the spatial receptive fields.

关键词： adaptive coding biological system modeling random codes image coding nonlinear circuits Biological system modeling random codes Adaptive coding Nonlinear circuits image coding Neurosciences sparse coding Neurophysiology COMPRESSED

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Factorized Latent Spaces with Structured Sparsity 10

Factorized Latent Spaces with Structured Sparsity

引用

Annual Conference on Neural Information Processing Systems

作者： Yangqing Jia Mathieu Salzmann Trevor Darrell UC Berkeley EECS and ICSI UC Berkeley EECS and ICSI TTI-Chicago

ISBN: (纸本)9781617823800

Recent approaches to multi-view learning have shown that factorizing the information into parts that are shared across all views and parts that are private to each view could effectively account for the dependencies and independencies between the different input modalities. Unfortunately, these approaches involve minimizing non-convex objective functions. In this paper, we propose an approach to learning such factorized representations inspired by sparse coding techniques. In particular, we show that structured sparsity allows us to address the multi-view learning problem by alternately solving two convex optimization problems. Furthermore, the resulting factorized latent spaces generalize over existing approaches in that they allow having latent dimensions shared between any subset of the views instead of between all the views only. We show that our approach outperforms state-of-the-art methods on the task of human pose estimation.

关键词： View Objective function Learning sparse coding Subset pose estimation coding techniques(computer programming) Non-Convex

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EDGE PRESERVING SINGLE IMAGE SUPER RESOLUTION IN sparse ENVIRONMENT

EDGE PRESERVING SINGLE IMAGE SUPER RESOLUTION IN SPARSE ENVI...

引用

IEEE International Conference on Image Processing

作者： Srimanta Mandal Anil Kumar Sao School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)9781479923427

Quality of an image is associated with edge of the image. It is important to preserve the edge of the image while deriving high resolution (HR) image from low resolution (LR) image, also known as super-resolution (SR) problem. This paper proposes an edge preserving constraint, which preserve the edge information of image by minimizing the differences between edges of LR image and the edges of the reconstructed image (down-sampled version), in sparse coding based SR problem. Partial edge evidences, derived using 1-D processing of image, are used separately in the constraints. The experimental results show that proposed approach preserves the edges of image as well as outperforms objectively the existing SR approaches.

关键词： Image Super-resolution sparse coding Dictionary Edge evidence edge image images sparse coding Imagery (Psychotherapy) Dictionaries as Topic edges Preserves Image Resolving power SAVANNAH RIVER OPERATIONS OFFICE Strontium telecommunication standards cellular radio

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Histograms of sparse Codes for Object Detection

Histograms of Sparse Codes for Object Detection

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Xiaofeng Ren Deva Ramanan *** University of California

ISBN: (纸本)9781467364102

Object detection has seen huge progress in recent years, much thanks to the heavily-engineered Histograms of Oriented Gradients (HOG) features. Can we go beyond gradients and do better than HOG? We provide an affirmative answer by proposing and investigating a sparse representation for object detection, Histograms of sparse Codes (HSC). We compute sparse codes with dictionaries learned from data using K-SVD, and aggregate per-pixel sparse codes to form local histograms. We intentionally keep true to the sliding window framework (with mixtures and parts) and only change the underlying features. To keep training (and testing) efficient, we apply dimension reduction by computing SVD on learned models, and adopt supervised training where latent positions of roots and parts are given externally e.g. from a HOG-based detector. By learning and using local representations that are much more expressive than gradients, we demonstrate large improvements over the state of the art on the PASCAL benchmark for both root-only and part-based models.

关键词： Object detection histograms Codes Dictionaries as Topic sparse coding Gradient

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Using A New Approach in Deep Dictionary Learning to Handwriting Number Classification

Using A New Approach in Deep Dictionary Learning to Handwrit...

引用

International CSI Computer Conference, CSICC

作者： Azadeh Montazeri Mahboubeh Shamsi Rouhollah Dianat Department of Information Technology Faculty of Computer University of Qom Iran Department of Computer Faculty of Computer and Electrical Qom University of Technology Iran

ISBN: (数字)9781728159379

ISBN: (纸本)9781728159386

Dictionary learning and sparse representation is a successful mathematical model for data representation that achieves state-of-the-art performance in various fields such as pattern recognition, machine learning, computer vision. The study aims to improve the classification performance of state-of-the-art methods by using a multi- layer framework. This paper presents the new idea of "multi- layered K-singular value decomposition (MLK-SVD)" dictionary learning as a multi-layer method of classification and is evaluated on MNIST dataset. The new idea is a multi- layer dictionary learning method for classification tasks. In order to learn better features, the frame uses a tag consistent in the K-SVD algorithm to learn a discriminative dictionary for sparse coding. We also include information on labels in addition to class labels for training data.

关键词： handwriting recognition image representation learning (artificial intelligence) pattern classification singular value decomposition handwriting recognition Pattern recognition image representation Singular value decomposition Dictionaries as Topic Taxonomy Representation of data sparse coding sparse representation Learning

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sparse Representation based on Matrix Rank Minimization and K-Means Clustering for Recognition

Sparse Representation based on Matrix Rank Minimization and ...

引用

International Joint Conference on Neural Networks

作者： Long Ma Chunheng Wang Baihua Xiao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China 100190

ISBN: (纸本)9781467314886

In this paper, we propose a sparse coding algorithm based on matrix rank minimization and k-means clustering and for recognition. We consider the problem of removing the noise in the training samples and generating more samples at the same time. To accomplish this, we extended the matrix rank minimization problem to cope with complex data. Samples from the same class are segmented into several groups by k-means clustering algorithm, and matrix rank minimization is applied on the clustered data to separate the noises and recover the low-rank structures in the grouped data. An over-complete dictionary is constructed by connecting the low-rank structures and the training samples together to keep the samples diversity. sparse representation is operated based on this over-complete dictionary for recognition. Furthermore, a parameter is introduced to adjust the weighting of the coefficients that code the noises. We apply the proposed algorithm for character and face recognition. Experiments with improved performances validate the effectiveness of the proposed algorithm.

关键词： sparse representation Noise Recognition (Psychology) Rank of data matrices sparse coding Dictionaries as Topic

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Image Denoising Algorithm Based on Incoherent Dictionary Learning

Image Denoising Algorithm Based on Incoherent Dictionary Lea...

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第三十八届中国控制会议

作者： Jianjun Li Junhua Wang Junshan Li The institute of information science & technology Guangdong University of Foreign Studies South China Business College

Aiming at the deficiency of losing texture information in image filtering process, an image filtering algorithm based on uncorrelated dictionary learning is proposed. Firstly, the noisy image is divided into overlapping image blocks, and the image blocks are randomly extracted. The uncorrelated redundant dictionary is obtained by using uncorrelated dictionary learning technology. Finally, the sparse representation coefficients of each image block under the redundant dictionary are obtained by sparse coding algorithm, and the original image is restored by using the sparse representation coefficients. The experimental results show that the irrelevant redundant dictionary has a strong ability to express the texture information of the image. It can keep the details and texture information of the image better and improve the visual effect.

关键词： Dictionary learning Incoherent Redundant dictionary sparse coding Image denoising

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IMAGE RETRIEVAL WITH HIERARCHICAL MATCHING PURSUIT

IMAGE RETRIEVAL WITH HIERARCHICAL MATCHING PURSUIT

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IEEE International Conference on Image Processing

作者： Shasha Bu Yu-Jin Zhang Department of Electronic Engineering Tsinghua University Tsinghua National Laboratory for Information Science and Technology

ISBN: (纸本)9781479957521

A novel representation of images for image retrieval is introduced in this paper, by using a new type of feature with remarkable discriminative power. Despite the multi-scale nature of objects, most existing models perform feature extraction on a fixed scale, which will inevitably degrade the performance of the whole system. Motivated by this, we introduce a hierarchical sparse coding architecture for image retrieval to explore multi-scale cues. sparse codes extracted on lower layers are transmitted to higher layers recursively. With this mechanism, cues from different scales are fused. Experiments on the Holidays dataset show that the proposed method achieves an excellent retrieval performance with a small code length.

关键词： CBIR sparse coding Hierarchical matching pursuit Bag-of-features

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A GREEDY ALGORITHM FOR MODEL SELECTION OF TENSOR DECOMPOSITIONS

A GREEDY ALGORITHM FOR MODEL SELECTION OF TENSOR DECOMPOSITI...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Austin J. Brockmeier Jose C. Principe Anh Huy Phan Andrzej Cichocki Electrical and Computer Engineering Department University of Florida Gainesville FL Brain Science Institute RIKEN Wako-shi Saitama Japan

ISBN: (纸本)9781479903573

Various tensor decompositions use different arrangements of factors to explain multi-way data. Components from different decompositions can vary in the number of parameters. Allowing a model to contain components from different decompositions results in a combinatoric number of possible models. Model selection balances approximation error and the number of parameters, but due to the number of possible models, post-hoc model selection is infeasible. Instead, we incrementally build a model. This approach is analogous to sparse coding with a union of dictionaries. The proposed greedy approach can estimate a model consisting of a combination of tensor decompositions.

关键词： Tensor decompositions Greedy algorithm Model selection model selection greedy algorithm Tensor sparse coding Approximation error algorithms

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Fast exemplar selection algorithm for matrix approximation and representation: A variant oASIS algorithm

Fast exemplar selection algorithm for matrix approximation a...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： V. Abrol P. Sharma A. K. Sao School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)9781509041183

Extracting inherent patterns from large data using decompositions of data matrix by a sampled subset of exemplars has found many applications in machine learning. We propose a computationally efficient algorithm for adaptive exemplar sampling, called fast exemplar selection (FES). The proposed algorithm can be seen as an efficient variant of the oASIS algorithm [1]. FES iteratively selects incoherent exemplars based on the exemplars that are already sampled. This is done by ensuring that the selected exemplars forms a positive definite Gram matrix which is checked by exploiting its Cholesky factorization in an incremental manner. FES is a deterministic rank revealing algorithm delivering a tighter matrix approximation bound. Further, FES can also be used to exactly represent low rank matrices and signals sampled from a unions of independent subspaces. Experimental results show that FES performs comparable to existing methods for tasks such as matrix approximation, feature selection, outlier detection, and clustering.

关键词： Matrix factorization exemplar selection low rank approximation sparse coding matrix decomposition sparse coding test sample FES gene selection algorithm matrices Lower Approximation rank of matrix Oases Cholesky factorisation approximation efficient algorithm

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