# Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms

arXiv: https://arxiv.org/abs/1707.02931 · HTML: https://mlsanity.com/paper/1707.02931/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching](https://mlsanity.com/paper/2203.15285/) (2022)
- [Semantic Line Combination Detector](https://mlsanity.com/paper/2404.18399/) (2024)
- [Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds](https://mlsanity.com/paper/2106.16129/) (2021)
- [Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images](https://mlsanity.com/paper/2201.06594/) (2022)
- [ArchSym: Detecting 3D-Grounded Architectural Symmetries in the Wild](https://mlsanity.com/paper/2604.22202/) (2026)
- [Classical Versus Deep Mirror-Symmetry Scoring: A Benchmark of Thirteen Methods](https://mlsanity.com/paper/2607.08379/) (2026)
- [Water Reflection Detection Using Symmetric Attention](https://mlsanity.com/paper/2607.10749/) (2026)

## Abstract (arXiv)

Symmetry is one of the significant visual properties inside an image plane, to identify the geometrically balanced structures through real-world objects. Existing symmetry detection methods rely on descriptors of the local image features and their neighborhood behavior, resulting incomplete symmetrical axis candidates to discover the mirror similarities on a global scale. In this paper, we propose a new reflection symmetry detection scheme, based on a reliable edge-based feature extraction using Log-Gabor filters, plus an efficient voting scheme parameterized by their corresponding textural and color neighborhood information. Experimental evaluation on four single-case and three multiple-case symmetry detection datasets validates the superior achievement of the proposed work to find global symmetries inside an image.