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Unsupervised Feature Extraction – A CNN-Based Approach

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https://hdl.handle.net/10037/17792
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Dato
2019-05-12
Type
Peer reviewed
Book
Chapter

Forfatter
Trosten, Daniel Johansen; Sharma, Puneet
Sammendrag
Working with large quantities of digital images can often lead to prohibitive computational challenges due to their massive number of pixels and high dimensionality. The extraction of compressed vectorial representations from images is therefore a task of vital importance in the field of computer vision. In this paper, we propose a new architecture for extracting such features from images in an unsupervised manner, which is based on convolutional neural networks. The model is referred to as the Unsupervised Convolutional Siamese Network (UCSN), and is trained to embed a set of images in a vector space, such that local distance structure in the space of images is approximately preserved. We compare the UCSN to several classical methods by using the extracted features as input to a classification system. Our results indicate that the UCSN produces vectorial representations that are suitable for classification purposes.
Forlag
Springer Nature
Sitering
Trosten, D.J.; Sharma, P. (2019) Unsupervised Feature Extraction – A CNN-Based Approach. I: Felsberg, M, Forssén, P.E.., Sintorn, I.M.; Unger, J. 21st Scandinavian Conference on Image Analysis, SCIA, 2019, Springer, Lecture Notes in Computer Science, vol 11482,, 197-208.
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  • Artikler, rapporter og annet (fysikk og teknologi) [1057]
Copyright 2019 Springer Nature

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