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Context-Based Object Recognition: Indoor Versus Outdoor Environments

Alameer, A; Degenaar, P; Nazarpour, K

Authors

Dr Ali Alameer A.Alameer1@salford.ac.uk
Lecturer in Artificial Intelligence

P Degenaar

K Nazarpour



Contributors

K Arai
Editor

S Kapoor
Editor

Abstract

Object recognition is a challenging problem in high-level vision. Models that perform well for the outdoor domain, perform poorly in the indoor domain and the reverse is also true. This is due to the dramatic discrepancies of the global properties of each environment, for instance, backgrounds and lighting conditions. Here, we show that inferring the environment before or during the recognition process can dramatically enhance the recognition performance. We used a combination of deep and shallow models for object and scene recognition, respectively. Also, we used three novel topologies that can provide a trade-off between classification accuracy and decision sensitivity. We achieved a classification accuracy of 97.91%, outperforming the performance of a single GoogLeNet by 13%. In another experiment, we achieved an accuracy of 95% to categorise indoor and outdoor scenes by inference.

Citation

Alameer, A., Degenaar, P., & Nazarpour, K. (2019). Context-Based Object Recognition: Indoor Versus Outdoor Environments. In K. Arai, & S. Kapoor (Eds.), CVC 2019: Advances in Computer Vision (437-490). Springer Nature. https://doi.org/10.1007/978-3-030-17798-0_38

Online Publication Date Apr 24, 2019
Publication Date Apr 24, 2019
Deposit Date May 26, 2022
Pages 437-490
Series Title Advances in Intelligent Systems and Computing
Series Number 944
Book Title CVC 2019: Advances in Computer Vision
ISBN 9783030177973
DOI https://doi.org/10.1007/978-3-030-17798-0_38
Publisher URL https://doi.org/10.1007/978-3-030-17798-0_38