A fuzzy membership recognition method for color correlation of small sample fire images
A small-sample fuzzy membership recognition method based on color correlation of fire image is proposed to address the problem of unsatisfactory recognition performance caused by the large sample data requirements and high computational complexity in fire image recognition.First,the size of the color deviation factor between the target image and the reference images is calculated,and the Romanovsky criterion optimization is performed to determine the category of the target image based on the sorting of the color deviation factor vector from small to large.Second,the gray comprehensive correlation degree is calculated based on the relative correlation degree,the proximity correlation degree,and the weight solving,and the membership degree of the target image to the same category of fire images is determined.Finally,based on the maximum membership principle and the image category threshold,the image is judged whether it belongs to a fire image.The results show that the proposed method achieves an overall recognition accuracy of 88.89%for fire images and 100%for non-fire images.Under sunny outdoor conditions,natural light interferes with the color characteristics of fire images with blue backgrounds.Under outdoor cloudy conditions,natural light interferes with the color characteristics of fire images with green backgrounds.However,natural light does not interfere with the color characteristics of fire images with red backgrounds in the above two scenarios,achieving 100%recognition accuracy.Under dark box conditions without light,the overall recognition accuracy for fire images with red,green,and blue backgrounds all reach 100%,indicating that natural light interferes with the color characteristics of fire images.The gray comprehensive correlation degree of fire images with different color backgrounds shows noticeable differences;for fire images with red and blue backgrounds,the contribution of relative correlation degree is significantly higher than that of proximity correlation degree;while for fire images with green backgrounds,the contribution of proximity correlation degree is significantly higher than that of relative correlation degree.The proposed method has higher recognition accuracy and lower computational cost for small-sample images compared with decision trees,support vector machines,and deep neural network algorithms.
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