Computer Science ›› 2015, Vol. 42 ›› Issue (1): 293-296.doi: 10.11896/j.issn.1002-137X.2015.01.065

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Classification of Multi-instance Image Based on Sparse Representation

SONG Xiang-fa and JIAO Li-cheng   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In order to effectively solve the problem of multi-instance image classification,a novel classification method of multi-instance image was proposed which is based on sparse representation.The whole image is regarded as a bag and each region as an instance of that bag.The image bag feature is computed based on instance embedded strategy.Next,the test image bag feature is regarded as sparse linear combination of training image bag feature set and the sparse solution can be obtained by 1 optimization method.Finally,sparse coefficients are utilized to predict the label of the test ima-ge.Experimental results on the Corel image data show that the proposed method is superior to the state-of-art methods in terms of classification accuracy.

Key words: Image classification,Multi-instance learning,Sparse representation

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