# A Discriminative Representation of Convolutional Features for Indoor Scene Recognition

arXiv: https://arxiv.org/abs/1506.05196 · HTML: https://mlsanity.com/paper/1506.05196/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2015.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for Scene Classification: A Survey](https://mlsanity.com/paper/2101.10531/) (2021)
- [InstaIndoor and Multi-modal Deep Learning for Indoor Scene Recognition](https://mlsanity.com/paper/2112.12409/) (2021)
- [Recent Advances in Scene Image Representation and Classification](https://mlsanity.com/paper/2206.07326/) (2022)
- [Fast and Efficient Scene Categorization for Autonomous Driving using VAEs](https://mlsanity.com/paper/2210.14981/) (2022)
- [EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition](https://mlsanity.com/paper/2307.12442/) (2023)
- [Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks](https://mlsanity.com/paper/2107.11187/) (2021)

## Abstract (arXiv)

Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities. This paper presents a novel approach which exploits rich mid-level convolutional features to categorize indoor scenes. Traditionally used convolutional features preserve the global spatial structure, which is a desirable property for general object recognition. However, we argue that this structuredness is not much helpful when we have large variations in scene layouts, e.g., in indoor scenes. We propose to transform the structured convolutional activations to another highly discriminative feature space. The representation in the transformed space not only incorporates the discriminative aspects of the target dataset, but it also encodes the features in terms of the general object categories that are present in indoor scenes. To this end, we introduce a new large-scale dataset of 1300 object categories which are commonly present in indoor scenes. Our proposed approach achieves a significant performance boost over previous state of the art approaches on five major scene classification datasets.