# Recent Advances in Features Extraction and Description Algorithms: A Comprehensive Survey

arXiv: https://arxiv.org/abs/1703.06376 · HTML: https://mlsanity.com/paper/1703.06376/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2017.

## Built on by (most cited first, 8 of 8)

- [BALF: Simple and Efficient Blur Aware Local Feature Detector](https://mlsanity.com/paper/2211.14731/) (2022)
- [An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2105.12763/) (2021)
- [Visual Correspondence Hallucination](https://mlsanity.com/paper/2106.09711/) (2021)
- [Scale-Free Image Keypoints Using Differentiable Persistent Homology](https://mlsanity.com/paper/2406.01315/) (2024)
- [PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data](https://mlsanity.com/paper/2103.06552/) (2021)
- [Error-Covariance Analysis of Monocular Pose Estimation Using Total Least Squares](https://mlsanity.com/paper/2210.12157/) (2022)
- [Invariant Scattering Transform for Medical Imaging](https://mlsanity.com/paper/2304.10582/) (2023)
- [RHVI-FDD: A Hierarchical Decoupling Framework for Low-Light Image Enhancement](https://mlsanity.com/paper/2604.05781/) (2026)

## Abstract (arXiv)

Computer vision is one of the most active research fields in information technology today. Giving machines and robots the ability to see and comprehend the surrounding world at the speed of sight creates endless potential applications and opportunities. Feature detection and description algorithms can be indeed considered as the retina of the eyes of such machines and robots. However, these algorithms are typically computationally intensive, which prevents them from achieving the speed of sight real-time performance. In addition, they differ in their capabilities and some may favor and work better given a specific type of input compared to others. As such, it is essential to compactly report their pros and cons as well as their performances and recent advances. This paper is dedicated to provide a comprehensive overview on the state-of-the-art and recent advances in feature detection and description algorithms. Specifically, it starts by overviewing fundamental concepts. It then compares, reports and discusses their performance and capabilities. The Maximally Stable Extremal Regions algorithm and the Scale Invariant Feature Transform algorithms, being two of the best of their type, are selected to report their recent algorithmic derivatives.