Filtering Method for Feature Maps from Convolutional Neural Network for Thermal Imaging Data
Commonly used visible light image data may fail because of harsh weather or poor light conditions;however,infrared thermal imaging data can effectively complement visible light image data.Existing studies often rely on Domain Adaptation(DA)to apply Convolutional Neural Network(CNN)to visible light image data to process thermal imaging data and overcome the lack of a large annotated training set for infrared data.However,DA methods cannot completely avoid the training process.Researchers have found that the domain-invariant and domain-variant components of an image can be separated in the frequency domain.Inspired by this phenomenon,a filtering method for feature maps from CNN based on Discrete Cosine Transform(DCT)and chi-square independence index is proposed.The domain-invariant and domain-variant components are separated in the frequency domain.By imitating the chi-square independence test,an independence index based on frequency components is proposed to measure the degree of difference in feature maps.According to this index,clustering is used to classify feature maps and identify the class(es)to be maintained or dropped.Thereafter,neural network suitable for thermal imaging data is constructed.The experimental results indicate that this method can determine the latent capabilities of pre-trained CNN for visible light images to extract the features of thermal imaging data without retraining the network.Although the pre-trained network failed to predict the thermal imaging data,the network constructed using the proposed method achieves up to 90％matching between the object and the top five prediction results.
thermal imaging dataDiscrete Cosine Transform(DCT)Domain Adaptation(DA)Convolutional Neural Network(CNN)traffic scene
万方数据
维普
