Deep stacked sparse auto-encoder based on patches for image classification

Intidhar Jemel; Salima Hassairi; Ridha Ejbali; Mourad Zaied

doi:10.1117/12.2559849

31 January 2020 Deep stacked sparse auto-encoder based on patches for image classification

Intidhar Jemel, Salima Hassairi, Ridha Ejbali, Mourad Zaied

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114331P (2020) https://doi.org/10.1117/12.2559849
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Image classification is an area where deep learning and especially stacked Auto-encoders have really proven their strength. The contributions of this paper lie in the creation of a new classifier to remedy some classification problems. This new method of classification presents a combination of the most used techniques in Deep Learning (DL) and Sparse Coding (SC) in the field of classification. Proposed deep neural networks consist of three stacked Auto-encoders and a Softmax used as an outer layer for classification. The first Auto-encoder is created from a sparse representation of all images of the dataset. The sparse representation of all images represents the decoder part of the first Auto-encoder. Then the transpose of the matrix is applied to get the encoder part. Experiments performed on standard datasets such as ImageNet and the Coil-100 reveal the efficacy of this approach.

Citation Download Citation

Intidhar Jemel, Salima Hassairi, Ridha Ejbali, and Mourad Zaied "Deep stacked sparse auto-encoder based on patches for image classification", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331P (31 January 2020); https://doi.org/10.1117/12.2559849

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