Deep learning for scene understanding

U. Nadeem; S.A.A. Shah; F. Sohel; R. Togneri; M. Bennamoun

doi:10.1007/978-3-030-11479-4_2

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Book chapter

Deep learning for scene understanding

U. Nadeem, S.A.A. Shah, F. Sohel, R. Togneri and M. Bennamoun

Advances in Computational Intelligence, Vol.509, pp.21-51

Springer

2019

DOI: 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.

Details

Title: Deep learning for scene understanding
Authors/Creators: U. Nadeem (Author/Creator) - The University of Western Australia
S.A.A. Shah (Author/Creator) - The University of Western Australia
F. Sohel (Author/Creator) - Murdoch University
R. Togneri (Author/Creator) - The University of Western Australia
M. Bennamoun (Author/Creator) - The University of Western Australia
Contributors: V. Balas (Editor)
S. Roy (Editor)
D. Sharma (Editor)
P. Samui (Editor)
Publication Details: Advances in Computational Intelligence, Vol.509, pp.21-51
Publisher: Springer
Identifiers: 991005544865107891
Murdoch Affiliation: School of Engineering and Information Technology
Language: English
Resource Type: Book chapter
Additional Information: Series title: Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, Vol 136

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