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With the emergence of autonomous indoor robots, the computer vision task ofindoor scene recognition has gained the spotlight. Indoor scene recognition isa challenging problem in computer vision that relies on local and globalfeatures in a scene. This study aims to compare the performance of five machinelearning algorithms on the task of indoor scene classification to identify thepros and cons of each classifier. It also provides a comparison of low latencyfeature extractors versus enormous feature extractors to understand theperformance effects. Finally, a simple MnasNet based indoor classificationsystem is proposed, which can achieve 72% accuracy at 23 ms latency.

An embarrassingly simple comparison of machine learning algorithms for indoor scene classification