<p>Early wildfire detection is important for managing fire to avoid severe damage to ecosystems and humans. Recently, computer vision-based fire detection approaches have become of interest to various researchers as they allow the monitoring of large areas, facilitating fire early detection. Generally, fire color is the main characteristic used in various rule-based approaches. Rule-based approaches determine fire pixels by assessing whether the rules are satisfied by exceeding a threshold value determined previously. In this research, we propose a rule-based wildfire pixel detection approach without thresholds. Our method estimates contrast characteristics of fire in various color spaces and combines the extracted features in a simple but still effective rule-based manner. The proposed approach consists of two main stages. Firstly, from various color spaces, the color-contrast estimation is performed and encoded into a set of gray-level images. Secondly, our proposed method combines various contrast features to obtain a final fire image. In contrast to the other methods, where a threshold is specified for the determination of fire pixels, our approach determines the fire pixel by using merely the color contrast features computed from the incoming image. We evaluated our proposal with ten rule-based and two learning-based fire detection methods. The results indicate that our proposal achieves competitive performance in three evaluation metrics.</p>