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

arXiv: https://arxiv.org/abs/2109.12261 · HTML: https://mlsanity.com/paper/2109.12261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 25 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition](https://mlsanity.com/paper/1609.00153/) (2016)
- [Dynamic texture and scene classification by transferring deep image features](https://mlsanity.com/paper/1502.00303/) (2015)
- [Towards Real-Time Action Recognition on Mobile Devices Using Deep Models](https://mlsanity.com/paper/1906.07052/) (2019)
- [A Novel Feature Extraction Method for Scene Recognition Based on Centered Convolutional Restricted Boltzmann Machines](https://mlsanity.com/paper/1506.07257/) (2015)

## Abstract (arXiv)

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.