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.
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Acknowledgments
The work of A. Alameer was supported by the Higher Committee for Education Development, Iraq (HCED, D1201017). The work of K. Nazarpour was supported by the Engineering and Physical Sciences Research Council, U.K., grants EP/M025977/1 and EP/M025594/1.
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Alameer, A., Degenaar, P., Nazarpour, K. (2020). Context-Based Object Recognition: Indoor Versus Outdoor Environments. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_38
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DOI: https://doi.org/10.1007/978-3-030-17798-0_38
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