Cluster-based multi-task Sparse Representation for efficient face recognition

Shafiee, Soheil; Kamangar, Farhad; Ghandehari, Laleh Sh.

doi:10.1109/ssiai.2014.6806045

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…The smooth self-representation learning clustering method was adopted in this study to further refine these subsets. The clustering reported by Shafiee et al was only for the training samples themselves [ 30 ]. Here, clustering was performed for both the testing sample and training samples.…”

Section: Aspect-aided Dynamic Active Atoms Selectionmentioning

confidence: 99%

Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

Zhang

Yang

Liu

et al. 2016

Sensors

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Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS) sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with ℓ1-regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR) database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance.

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Section: Aspect-aided Dynamic Active Atoms Selectionmentioning

confidence: 99%

Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

Zhang

Yang

Liu

et al. 2016

Sensors

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“…Here, since modality matrices come from equal number of columns, the problem can be approached similar to MTJSRC by directly applying (4) to recover the sparse coefficients. This method is shown to be effective in [13] where adaptive clustering was incorporated in a multi-modality framework. Despite the interesting results reported for this approach, due to the different natures of modalities in terms of information overlaps and redundancies, the optimum number of representatives may vary among different modalities.…”

Section: Problem Formulationmentioning

confidence: 99%

“…This problem is even more intense in this case since there are multiple training matrices each of them imposing its own time complexity to the solution. To tackle this problem, [13] and [12] propose to incorporate dictionary learning in MTJSRC and reported improved results on face recognition.…”

Section: Introductionmentioning

confidence: 99%

A Multi-modal Sparse Coding Classifier Using Dictionaries with Different Number of Atoms

Shafiee

Kamangar

Athitsos

2015

2015 IEEE Winter Conference on Applications of Computer Vision

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Most of classification methods including the ones based on sparse representation (SRC), look at every training sample and its extracted modalities as a single point in a high dimensional space and a collection of these points build the training space used to train the classifier. In a multimodality classification problem, there might be lots of redundancies associated with different modalities of the training data which degrade the performance of the classifier. This paper considers the problem of multi-modality classification in a sparse representation framework which separately looks at each modality space and builds abstract and in the same time, precise models with different number of representatives for each individual modality. An optimization is also introduced to solve the classification problem using these models. In comparison to SRC which directly uses one modality from the training samples, the proposed method utilizes multiple abstract modalities to form efficient and comprehensive representation of the data in order to increase both the accuracy and efficiency of the classification process. Experimental results on face and digit recognition applications show that the proposed method has higher recognition rate compared to single-modality as well as multi-modality methods based on SRC.

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Deconstructive Human Confront Acknowledgment Utilizing Profound Neural Arrange

Dubey

Choubey

2022

Information Systems and Management Science

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Cluster-based multi-task Sparse Representation for efficient face recognition

Cited by 5 publications

References 9 publications

Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

A Multi-modal Sparse Coding Classifier Using Dictionaries with Different Number of Atoms

Deconstructive Human Confront Acknowledgment Utilizing Profound Neural Arrange

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