# BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis

arXiv: https://arxiv.org/abs/1506.03495 · HTML: https://mlsanity.com/paper/1506.03495/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2015.

## Built on by (most cited first, 16 of 16)

- [A Novel Disaster Image Dataset and Characteristics Analysis using Attention Model](https://mlsanity.com/paper/2107.01284/) (2021)
- [From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios](https://mlsanity.com/paper/2506.20279/) (2025)
- [Attention on Classification for Fire Segmentation](https://mlsanity.com/paper/2111.03129/) (2021)
- [Real-Time Aerial Fire Detection on Resource-Constrained Devices Using Knowledge Distillation](https://mlsanity.com/paper/2502.20979/) (2025)
- [Benchmarking Multi-Scene Fire and Smoke Detection](https://mlsanity.com/paper/2410.16631/) (2024)
- [Fire and Smoke Detection with Burning Intensity Representation](https://mlsanity.com/paper/2410.16642/) (2024)
- [Fire Threat Detection From Videos with Q-Rough Sets](https://mlsanity.com/paper/2101.08459/) (2021)
- [Analysis of Deep Learning Architectures and Efficacy of Detecting Forest Fires](https://mlsanity.com/paper/2212.04030/) (2022)
- [FSDNet-An efficient fire detection network for complex scenarios based on YOLOv3 and DenseNet](https://mlsanity.com/paper/2304.07584/) (2023)
- [Get the Ball Rolling: Alerting Autonomous Robots When to Help to Close the Healthcare Loop](https://mlsanity.com/paper/2311.02602/) (2023)
- [Fire Dynamic Vision: Image Segmentation and Tracking for Multi-Scale Fire and Plume Behavior](https://mlsanity.com/paper/2408.08984/) (2024)
- [AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions](https://mlsanity.com/paper/2507.01547/) (2025)
- [D3-Predictor: Noise-Free Deterministic Diffusion for Dense Prediction](https://mlsanity.com/paper/2512.07062/) (2025)
- [AusSmoke meets MultiNatSmoke: a fully-labelled diverse smoke segmentation dataset](https://mlsanity.com/paper/2604.23542/) (2026)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)
- [SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLMs](https://mlsanity.com/paper/2609.07823/) (2026)

## Abstract (arXiv)

Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.