# Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces

arXiv: https://arxiv.org/abs/2102.00841 · HTML: https://mlsanity.com/paper/2102.00841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Kymatio: Scattering Transforms in Python](https://mlsanity.com/paper/1812.11214/) (2018)
- [Deep Scattering Spectrum](https://mlsanity.com/paper/1304.6763/) (2013)
- [Dynamic texture and scene classification by transferring deep image features](https://mlsanity.com/paper/1502.00303/) (2015)
- [A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition](https://mlsanity.com/paper/1708.06690/) (2017)
- [Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields](https://mlsanity.com/paper/1710.04842/) (2017)
- [Alignment Distances on Systems of Bags](https://mlsanity.com/paper/1706.04388/) (2017)

## Abstract (arXiv)

Distance-based dynamic texture recognition is an important research field in multimedia processing with applications ranging from retrieval to segmentation of video data. Based on the conjecture that the most distinctive characteristic of a dynamic texture is the appearance of its individual frames, this work proposes to describe dynamic textures as kernelized spaces of frame-wise feature vectors computed using the Scattering transform. By combining these spaces with a basis-invariant metric, we get a framework that produces competitive results for nearest neighbor classification and state-of-the-art results for nearest class center classification.