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.
Original languageEnglish
Title of host publicationHandbook of deep learning applications
EditorsValentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui
Place of PublicationCham
PublisherSpringer
Pages21-51
Number of pages27
ISBN (Electronic)9783030114794
ISBN (Print)9783030114787
DOIs
Publication statusPublished - 19 Mar 2019

Publication series

NameSmart Innovation, Systems and Technologies
PublisherSpringer International Publishing
Volume136
ISSN (Electronic)2190-3018

Fingerprint Dive into the research topics of 'Deep learning for scene understanding'. Together they form a unique fingerprint.

Cite this