Abstract
Based on advantages of basic non-negative sparse coding (NNSC) model, and considered the prior class constraint of image features, a novel NNSC model is discussed here. In this NNSC model, the sparseness criteria is selected as a two-parameter density estimation model and the dispersion ratio of within-class and between-class is used as the class constraint. Utilizing this NNSC model, image features can be extracted successfully. Further, the feature recognition task by using different classifiers can be implemented well. Simulation results prove that our NNSC model proposed is indeed effective in extracting image features and recognition task in application.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Nos. 61373098, 61370109).
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Wang, X., Wang, C., Shang, L. et al. Dispersion Constraint Based Non-negative Sparse Coding Model. Neural Process Lett 43, 603–609 (2016). https://doi.org/10.1007/s11063-015-9432-7
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DOI: https://doi.org/10.1007/s11063-015-9432-7