Image Classification Method Based on Distribution Structure Constrain Sparse Representation

doi:10.3969/j.issn.1006-2475.2015.07.016

Abstract

Abstract:

To solve the structure information loss issue on sparse representation for accurate image classification, a new method based on structure constrain sparse

representation was proposed. The training samples after downsampling and extracting histogram of orientated gradient (Hog) were utilized to construct sparse linear coding model.

The sparse coefficients were solved on the training samples by distribution structure information constrain and 1minimization, and image was classified by sparse coefficient

mean. Experimental results with COREL dataset demonstrated that the proposed method can obtain the good recognition performance. Comparing with nonstructure constrain sparse

representation, the proposed method greatly improves the accuracy of image classification.

Key words: distribution structure constrain, sparse representation, image classification

CLC Number:

TP391.41

FAN Yindi. Image Classification Method Based on Distribution Structure Constrain Sparse Representation[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.07.016.

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Image Classification Method Based on Distribution Structure Constrain Sparse Representation

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