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Deep learning for scene understanding

Nadeem, U., Shah, S.A.A., Sohel, F., Togneri, R. and Bennamoun, M. (2019) Deep learning for scene understanding. In: Balas, V., Roy, S., Sharma, D. and Samui, P., (eds.) Advances in Computational Intelligence. Springer, pp. 21-51.

Link to Published Version: https://doi.org/10.1007/978-3-030-11479-4_2
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Abstract

With the progress in the field of computer vision, we are moving closer and closer towards the ultimate aim of human like vision for machines. Scene understanding is an essential part of this research. It seeks the goal that any image should be as understandable and decipherable for computers as it is for humans. The stall in the progress of the different components of scene understanding, due to the limitations of the traditional algorithms, has now been broken by the induction of neural networks for computer vision tasks. The advancements in parallel computational hardware has made it possible to train very deep and complex neural network architectures. This has vastly improved the performances of algorithms for all the different components of scene understanding. This chapter analyses these contributions of deep learning and also presents the advancements of high level scene understanding tasks, such as caption generation for images. It also sheds light on the need to combine these individual components into an integrated system.

Item Type: Book Chapter
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Springer
Copyright: © 2019 Springer Nature Switzerland AG
Other Information: Series title: Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, Vol 136
URI: http://researchrepository.murdoch.edu.au/id/eprint/44572
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