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

TL;DR

This paper compares five machine learning algorithms for indoor scene classification, analyzing feature extractor impacts, and proposes a simple MnasNet-based system achieving 72% accuracy with low latency.

Contribution

It provides a straightforward comparison of classifiers and feature extractors, and introduces a lightweight MnasNet-based indoor scene classification system.

Findings

MnasNet-based system achieves 72% accuracy

Low latency feature extractors perform competitively

Comparison highlights pros and cons of classifiers

Abstract

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.