压缩感知稀疏识别用于多视角图像目标分类

doi:10.3969/j.issn.0255-8297.2013.02.012

Abstract

Abstract: Multi-view image target classification is usually difficult. To deal with the problem, we propose a sparse recognition (SR) method with compressed sensing (CS) features. Sensing data of the original image are used as corresponding features. Both the test sample and the training sample set are integrated into an SR framework with their CS features. Classification results can be obtained by solving an l1-norm optimization problem. Experiments show that excellent performance of SR can be obtained by using CS features that retain information redundancy of the original sample. Meanwhile, multi-view image target classification is robust without preprocessing.

Key words: image target classification, multi-view, compressed sensing, sparse recognition

CLC Number:

TP391.4

LIU Ji-xin, SUN Quan-sen, CAO Guo. Compressed Sensing Sparse Recognition for Target Classification from Multi-view Images[J]. Journal of Applied Sciences, 2013, 31(2): 177-182.

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Compressed Sensing Sparse Recognition for Target Classification from Multi-view Images

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