# Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings

arXiv: https://arxiv.org/abs/1908.06422 · HTML: https://mlsanity.com/paper/1908.06422/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition](https://mlsanity.com/paper/2108.00399/) (2021)
- [BORM: Bayesian Object Relation Model for Indoor Scene Recognition](https://mlsanity.com/paper/2108.00397/) (2021)
- [Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification](https://mlsanity.com/paper/2301.00145/) (2022)
- [SRRM: Semantic Region Relation Model for Indoor Scene Recognition](https://mlsanity.com/paper/2305.08540/) (2023)
- [TbExplain: A Text-based Explanation Method for Scene Classification Models with the Statistical Prediction Correction](https://mlsanity.com/paper/2307.10003/) (2023)
- [Inter-object Discriminative Graph Modeling for Indoor Scene Recognition](https://mlsanity.com/paper/2311.05919/) (2023)
- [EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition](https://mlsanity.com/paper/2307.12442/) (2023)
- [Semantic-guided modeling of spatial relation and object co-occurrence for indoor scene recognition](https://mlsanity.com/paper/2305.12661/) (2023)
- [Semantic-embedded Similarity Prototype for Scene Recognition](https://mlsanity.com/paper/2308.05896/) (2023)

## Abstract (arXiv)

Scene Classification has been addressed with numerous techniques in computer vision literature. However, with the increasing number of scene classes in datasets in the field, it has become difficult to achieve high accuracy in the context of robotics. In this paper, we implement an approach which combines traditional deep learning techniques with natural language processing methods to generate a word embedding based Scene Classification algorithm. We use the key idea that context (objects in the scene) of an image should be representative of the scene label meaning a group of objects could assist to predict the scene class. Objects present in the scene are represented by vectors and the images are re-classified based on the objects present in the scene to refine the initial classification by a Convolutional Neural Network (CNN). In our approach we address indoor Scene Classification task using a model trained with a reduced pre-processed version of the Places365 dataset and an empirical analysis is done on a real-world dataset that we built by capturing image sequences using a GoPro camera. We also report results obtained on a subset of the Places365 dataset using our approach and additionally show a deployment of our approach on a robot operating in a real-world environment.