We address the challenging problem of face recognition under the scenarios where both training and test data are possibly contaminated with spatial misalignments. A supervised sparse coding framework is developed in t...
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We address the challenging problem of face recognition under the scenarios where both training and test data are possibly contaminated with spatial misalignments. A supervised sparse coding framework is developed in this paper towards a practical solution to misalignment-robust face recognition. Each gallery face image is represented as a set of patches, in both original and misaligned positions and scales, and each given probe face image is then uniformly divided into a set of local patches. We propose to sparsely reconstruct each probe image patch from the patches of all gallery images, and at the same time the reconstructions for all patches of the probe image are regularized by one term towards enforcing sparsity on the subjects of those selected patches. The derived reconstruction coefficients by l(1)-norm minimization are then utilized to fuse the subject information of the patches for identifying the probe face. Such a supervised sparse coding framework provides a unique solution to face recognition with all (Here, we emphasize "all" because some conventional algorithms for face recognition possess partial of these characteristics.) the following four characteristics: (1) the solution is model-free, without the model learning process, (2) the solution is robust to spatial misalignments, (3) the solution is robust to image occlusions, and (4) the solution is effective even when there exist spatial misalignments for gallery images. Extensive face recognition experiments on three benchmark face datasets demonstrate the advantages of the proposed framework over holistic sparsecoding and conventional subspace learning based algorithms in terms of robustness to spatial misalignments and image occlusions. (C) 2012 Elsevier Inc. All rights reserved.
Task functional magnetic resonance imaging (fMRI) has been widely employed for brain activation detection and brain network analysis. Modeling rich information from spatially-organized collection of fMRI time series i...
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Task functional magnetic resonance imaging (fMRI) has been widely employed for brain activation detection and brain network analysis. Modeling rich information from spatially-organized collection of fMRI time series is challenging because of the intrinsic complexity. Hypothesis-driven methods, such as the general linear model (GLM), which regress exterior stimulus from voxel-wise functional brain activity, are limited due to overlooking the complexity of brain activities and the diversity of concurrent brain networks. Recently, sparse representation and dictionary learning methods have attracted increasing interests in task fMRI data analysis. The major advantage of this methodology is its promise in reconstructing concurrent brain networks systematically. However, this data-driven strategy is, to some extent, arbitrary and does not sufficiently utilize the prior information of task design and neuroscience knowledge. To bridge this gap, we here propose a novel supervisedsparse representation and dictionary learning framework based on stochastic coordinate coding (SCC) algorithm for task fMRI data analysis, in which certain brain networks are learned with known information such as pre-defined temporal patterns and spatial network patterns, and at the same time other networks are learned automatically from data. Our proposed method has been applied to two independent task fMRI datasets, and qualitative and quantitative evaluations have shown that our method provides a new and effective framework for task fMRI data analysis. (C) 2017 Elsevier B.V. All rights reserved.
作者：
Dong, JianSun, ChangyinYang, WankouSoutheast Univ
Sch Automat Nanjing 210096 Jiangsu Peoples R China Southeast Univ
Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Jiangsu Peoples R China Southeast Univ
Jiangsu Key Lab Image & Video Understanding Socia Nanjing 210096 Jiangsu Peoples R China
In this paper, we propose a supervised dictionary learning algorithm for action recognition in still images followed by a discriminative weighting model. The dictionary is learned based on Local Fisher Discrimination ...
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In this paper, we propose a supervised dictionary learning algorithm for action recognition in still images followed by a discriminative weighting model. The dictionary is learned based on Local Fisher Discrimination which takes into account the local manifold structure and discrimination information of local descriptors. The label information of local descriptors is considered in both dictionary learning and sparsecoding stage which generates a supervised sparse coding algorithm and makes the coding coefficients discriminative. Instead of using spatial pyramid features, sliding window-based features with max-pooling are computed from coding coefficients. And then a discriminative weighting model combining a max-margin classifier is proposed using the features. Both the weighting coefficients and model parameters can be jointly learned using the same way in Multiple Kernel Learning algorithm. We validate our model on the following action recognition datasets: Willow 7 human actions dataset, People Playing Music Instrument (PPMI) dataset, and Sports dataset. To show the generality of our model, we also validate it on Scene15 dataset. The experiment results show that only with single scale local descriptors, our algorithm is comparable to some state-of-the-art algorithms. (C) 2015 Elsevier B.V. All rights reserved.
This paper proposes to learn a discriminative dictionary for saliency detection. In addition to the conventional sparsecoding mechanism that learns a representational dictionary of natural images for saliency predict...
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ISBN:
(纸本)9781479906529
This paper proposes to learn a discriminative dictionary for saliency detection. In addition to the conventional sparsecoding mechanism that learns a representational dictionary of natural images for saliency prediction, this work uses supervised information from eye tracking experiments in training to enhance the discriminative power of the learned dictionary. Furthermore, we explicitly model saliency at multi-scale by formulating it as a multi-class problem, and a label consistency term is incorporated into the framework to encourage class (salient vs. non-salient) and scale consistency in the learned sparse codes. K-SVD is employed as the central computational module to efficiently obtain the optimal solution. Experiments demonstrate the superior performance of the proposed algorithm compared with the state-of-the-art in saliency prediction.
This paper proposes to learn a discriminative dictionary for saliency detection. In addition to the conventional sparsecoding mechanism that learns a representational dictionary of natural images for saliency predict...
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ISBN:
(纸本)9781479906505
This paper proposes to learn a discriminative dictionary for saliency detection. In addition to the conventional sparsecoding mechanism that learns a representational dictionary of natural images for saliency prediction, this work uses supervised information from eye tracking experiments in training to enhance the discriminative power of the learned dictionary. Furthermore, we explicitly model saliency at multi-scale by formulating it as a multi-class problem, and a label consistency term is incorporated into the framework to encourage class (salient vs. non-salient) and scale consistency in the learned sparse codes. K-SVD is employed as the central computational module to efficiently obtain the optimal solution. Experiments demonstrate the superior performance of the proposed algorithm compared with the state-of-the-art in saliency prediction.
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