An embarrassingly simple comparison of machine learning algorithms for indoor scene classification
This work addresses indoor scene recognition for autonomous robots, but it is incremental as it primarily compares existing methods.
The study compared five machine learning algorithms for indoor scene classification, focusing on performance trade-offs, and proposed a MnasNet-based system that achieved 72% accuracy with 23 ms latency.
With the emergence of autonomous indoor robots, the computer vision task of indoor scene recognition has gained the spotlight. Indoor scene recognition is a challenging problem in computer vision that relies on local and global features in a scene. This study aims to compare the performance of five machine learning algorithms on the task of indoor scene classification to identify the pros and cons of each classifier. It also provides a comparison of low latency feature extractors versus enormous feature extractors to understand the performance effects. Finally, a simple MnasNet based indoor classification system is proposed, which can achieve 72% accuracy at 23 ms latency.