# Recognition of Geometrical Shapes by Dictionary Learning

arXiv: https://arxiv.org/abs/2504.10958 · HTML: https://mlsanity.com/paper/2504.10958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Sparsity Based Poisson Denoising with Dictionary Learning](https://mlsanity.com/paper/1309.4306/) (2013)

## Abstract (arXiv)

Dictionary learning is a versatile method to produce an overcomplete set of vectors, called atoms, to represent a given input with only a few atoms. In the literature, it has been used primarily for tasks that explore its powerful representation capabilities, such as for image reconstruction. In this work, we present a first approach to make dictionary learning work for shape recognition, considering specifically geometrical shapes. As we demonstrate, the choice of the underlying optimization method has a significant impact on recognition quality. Experimental results confirm that dictionary learning may be an interesting method for shape recognition tasks.