Abstract
In image classification task, several recent works show that sparse representation plays a basic role in dictionary learning. However, this approach neglects the spatial relationships in the image space during dictionary learning. However, this approach neglects the neighboring relationship in dictionary learning. To alleviate the impact of this problem, we propose a novel dictionary learning based on Laplacian sparse coding method that profits from the neighboring relationship among the local features. For that purpose, we incorporate the matching between local regions in the Laplacian sparse coding formula. Moreover, we integrate statistical analysis of the distribution of the responses of each local feature to the dictionary basis in the final image representation. Our experimental results prove that our method performs existing background results based on sparse representation.
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Dammak, M., Mejdoub, M., Amar, C.B. (2014). Extended Laplacian Sparse Coding for Image Categorization. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_36
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DOI: https://doi.org/10.1007/978-3-319-12643-2_36
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