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检索条件"主题词=sparse coding"

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sparse Representation of Group-Wise FMRI Signals

Sparse Representation of Group-Wise FMRI Signals

引用

16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Lv, Jinglei Li, Xiang Zhu, Dajiang Jiang, Xi Zhang, Xin Hu, Xintao Zhang, Tuo Guo, Lei Liu, Tianming Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Univ Georgia Dept Comp Sci Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Athens GA 30602 USA

ISBN: (纸本)9783642407604;9783642407598

The human brain function involves complex processes with population codes of neuronal activities. Neuroscience research has demonstrated that when representing neuronal activities, sparsity is an important characterizing property. Inspired by this finding, significant amount of efforts from the scientific communities have been recently devoted to sparse representations of signals and patterns, and promising achievements have been made. However, sparse representation of fMRI signals, particularly at the population level of a group of different brains, has been rarely explored yet. In this paper, we present a novel group-wise sparse representation of task-based fMRI signals from multiple subjects via dictionary learning methods. Specifically, we extract and pool task-based fMRI signals for a set of cortical landmarks, each of which possesses intrinsic anatomical correspondence, from a group of subjects. Then an effective online dictionary learning algorithm is employed to learn an over-complete dictionary from the pooled population of fMRI signals based on optimally determined dictionary size. Our experiments have identified meaningful Atoms of Interests (AOI) in the learned dictionary, which correspond to consistent and meaningful functional responses of the brain to external stimulus. Our work demonstrated that sparse representation of group-wise fMRI signals is naturally suitable and effective in recovering population codes of neuronal signals conveyed in fMRI data.

关键词： DTI Task-based fMRI sparse coding

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FAST ONLINE L₁-DICTIONARY LEARNING ALGORITHMS FOR NOVEL DOCUMENT DETECTION

FAST ONLINE L<sub>1</sub>-DICTIONARY LEARNING ALGORITHMS FOR...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kasiviswanathan, Shiva Prasad Gen Elect Res San Ramon CA 94583 USA

ISBN: (纸本)9781479903566

Online L-1-dictionary learning, introduced by Kasiviswanathan et al. [1], is the process of generating a sequence of (dictionary) matrices {A(t+1)}, one at a time, for t = 0, 1, .... After committing to A(t+1), a pair of matrices (Pt+1;Xt+1) is revealed and the online algorithm incurs a cost of parallel to Pt+1 - A(t+1)X(t+1)parallel to(1). The goal of the online algorithm is to ensure that the total cost up to each time is not much larger than the smallest total cost of any fixed A chosen with the benefit of hindsight. In this paper, we study three different algorithms for this problem based on the schemes of dual averaging, projected gradient, and alternating direction method of multipliers. We focus on the performance of these algorithms for the application of novel document detection, where online dictionary learning could be used to automatically identify emerging topics of discussion from a voluminous stream of text documents in a scalable manner. Our empirical results show the relative benefits of these three algorithms for this application.

关键词： Dictionary Learning sparse coding Online Algorithms Topic Detection

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COLOR DE-RENDERING USING COUPLED DICTIONARY LEARNING

COLOR DE-RENDERING USING COUPLED DICTIONARY LEARNING

引用

20th IEEE International Conference on Image Processing (ICIP)

作者： Rushdi, Muhammad Ali, Mohsen Ho, Jeffrey Univ Florida Comp & Informat Sci & Engn Gainesville FL 32611 USA

ISBN: (纸本)9781479923410

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

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A BOOSTING APPROACH TO LEARNING RECEPTIVE FIELDS FOR SCENE CATEGORIZATION

A BOOSTING APPROACH TO LEARNING RECEPTIVE FIELDS FOR SCENE C...

引用

20th IEEE International Conference on Image Processing (ICIP)

作者： Zhang, Hui Liu, Yi Xie, Bojun Yu, Jian Beijing Jiaotong Univ Sch Comp & Informat Technol Lab Machine Learning & Cognit Computat Beijing Peoples R China Hebei Univ Coll Math & Comp Sci Key Lab Machine Learning & Computat Intelligence Baoding Hebei Peoples R China

ISBN: (纸本)9781479923410

Recently, sparse coding-based algorithms have achieved high performance on several popular scene classification benchmarks. Yet extensive efforts along this direction focus on strategies for coding and dictionary learning, few works have addressed the problem of optimal pooling regions selection. In this work, we show that the Viola-Jones algorithm, which is well-known in face detection, can be tailored to learning receptive fields for the sparse coding algorithms. Specifically, using the boosting approach to receptive field learning, image/scene categorization performance can be ubiquitously enhanced on several benchmarks (UIUC sport event, 15 natural scenes and the Caltech 101 dataset) to the state-of-the-art, using only low dimensional features and small codebook sizes. Furthermore, the "salient pooling regions" can be obtained explicitly.

关键词： Scene categorization receptive fields learning boosting sparse coding spatial pyramid matching

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A ROBUST sparse REPRESENTATION FRAMEWORK FOR DEPTH MAP RESTORATION

A ROBUST SPARSE REPRESENTATION FRAMEWORK FOR DEPTH MAP RESTO...

引用

IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Liu, Xien Sun, Yanfeng Hu, Yongli Yin, Baocai Beijing Univ Technol Coll Comp Sci & Technol Beijing Key Lab Multimedia & Intelligent Software Beijing Peoples R China

ISBN: (纸本)9781479916030

Recently, techniques based on dictionary learning for sparse representation have demonstrated promising results for depth or disparity maps restoration. However, we show that these methods are not robust due to the fact that depth or disparity maps are not only slightly contaminated by additive Gaussian noise but also seriously corrupted with outliers, occlusions, or even variable uncertainties. These seriously corrupted pixels not only lead to irregular structures obtained by dictionary but also seriously deteriorate the sparse coding effectiveness. To overcome these problems, in this paper we propose a new robust sparse representation framework to restore depth maps. In our proposed framework, seriously corrupted pixels can be automatically identified and their disturbance effects are gradually diminished through a few iterations. Thus, our proposed framework is more robust for depth restoration. Experimental results are presented to demonstrate the effectiveness of the proposed framework.

关键词： sparse coding dictionary learning depth restoration robust sparse representation

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AUDIO LOSSLESS coding/DEcoding METHOD USING BASIS PURSUIT ALGORITHM

AUDIO LOSSLESS CODING/DECODING METHOD USING BASIS PURSUIT AL...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： He, Wenxin Qu, Tianshu Peking Univ Speech & Hearing Res Ctr Minist Educ Key Lab Machine Percept Beijing 100871 Peoples R China

ISBN: (纸本)9781479903566

Basis pursuit algorithm is one of the most popular methods of sparse coding. The goal of the algorithm is to represent signal using as few coefficients as possible, which is suitable for acoustic signal compression. This paper presents a lossless coding/decoding method using the basis pursuit algorithm. In this method, wavelet packets bases were used to compose the dictionary because of their natural sparse property. Experimental results are obtained by comparing the proposed method with the four popular lossless coding/decoding methods using various types of acoustic signals. The results show that the proposed method is competitive with the well-known methods for lossless compression, in terms of compression ratio and computational efficiency.

关键词： Lossless coding/decoding Basis pursuit Wavelet packets transform sparse coding Lifting scheme

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A SCENE PARSING METHOD BASED ON SUPER-PIXEL AND MID-LEVEL FEATURE

A SCENE PARSING METHOD BASED ON SUPER-PIXEL AND MID-LEVEL FE...

引用

5th IEEE International Conference on Broadband Network & Multimedia Technology (IC-BNMT)

作者： Dong, Shidu Chongqing Univ Technol Coll Comp Sci Chongqing 400050 Peoples R China

ISBN: (纸本)9781479900930

Scene parsing can be formulated as a labelling problem that tries to label each pixel in an image with category of the object it belongs to, which involves the simultaneous detection, segmentation and recognition of all the objects in the image. A three stages method based on super-pixel and mid-level feature is proposed in this paper. First, super-pixels of the image are obtained by quick-shift. Second, the mid-level of each super-pixel are collected by aggregating the sift features in the super-pixel and its neighbor with sparse coding and maxpooling. Third, through CRF Models, which imposes consistency and coherency between labels, the globally optimal labeling results are obtained. Experimental results show that our method gains higher accuracy than previous methods.

关键词： Scene parsing Scene labelling sparse coding Max-pooling

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An Adaptive Orthogonal Matching Pursuit Algorithm Based on Redundancy Dictionary

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

引用

10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Tian, Yumin Wang, Zhihui Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China

ISBN: (纸本)9781467352536

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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sparse Model in Hierarchic Spatial Structure for Food Image Recognition

Sparse Model in Hierarchic Spatial Structure for Food Image ...

引用

6th International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Kusumoto, Riko Han, Xian-Hua Chen, Yen-Wei Ritsumeikan Univ Grad Sch Sci & Engn Shiga Japan

ISBN: (纸本)9781479927616

Recent year, with the increasing of unhealthy diets which will threaten people's life due to the various resulted risks such as heart stroke, liver trouble and so on, the remain for healthy life has attracted much attention and then how to manage the dietary life is becoming more and more important. In this research, we aim to construct a auto-recognition system of food images and keep the daily food-log records which will contribute to manage dietary life. With the easily available food images taken by mobile phone, it prospects to give the insight about the daily dietary of users with our constructed food recognition system. In order to achieve the acceptable recognition performance of the food images, we propose to apply a sparse model for coding a local descriptor extracted from the food images. sparse coding: an extension of vector quantization for local descriptors, which is popularly used in Bag-of-Features (BoF) for image representation in generic object recognition, can represent the local descriptors more efficient, and then abtain more discriminant feature for food image representation. Moreover, in order to introduce spatial information, a hierarchic spatial structure is explored to extract the feature based sparse model. Experiments validate that the proposed strategy can greatly improve the recognition rates compared with the conventional BOF model on two databases: our constructed RFID and the public PFID.

关键词： component formatting sparse coding food image recognation

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AUTOMATED IDENTIFICATION OF NEURONAL ACTIVITY FROM CALCIUM IMAGING BY sparse DICTIONARY LEARNING

AUTOMATED IDENTIFICATION OF NEURONAL ACTIVITY FROM CALCIUM I...

引用

IEEE 10th International Symposium on Biomedical Imaging - From Nano to Macro (ISBI)

作者： Diego, Ferran Reichinnek, Susanne Both, Martin Hamprecht, Fred A. Heidelberg Univ IWR HCI D-69115 Heidelberg Germany Heidelberg Univ Inst Physiol & Pathophysiol D-69120 Heidelberg Germany

ISBN: (纸本)9781467364553

We present ADINA, an automated pipeline for analyzing and identifying neuronal activity from calcium imaging data to investigate neuronal activity patterns. This entails the detection and classification of cell centroids and of calcium transients (events) that reappeared during different activity periods as memory consolidation. Specifically, the pipeline implements a sparse dictionary learning to infer the most relevant Ca2+ patterns, an image segmentation procedure using a wavelet-transform and watershed to identify single cells, and an estimation of the transient signals by means of sparse coding exploiting spatial and temporal sparsity. We validate our automated approach on artificial and two different calcium imaging sequences from mice hippocampal slice cultures acquired with fluorescence and confocal microscopes. Our approach achieves ca. 94% sensitivity on average for correctly detecting events, thus improving significantly the estimation of cell signals relative to published procedures.

关键词： Calcium Imaging Cell Sorting Dictionary Learning sparse coding Segmentation

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