A construction of heterogeneous transfer learning model based on associative fusion of image feature data

Source images and predicted target images differ in image features. When heterogeneous transfer learning is applied some difficulties and further issues appear. For example, noise in image recognition appears and is required to be reduced. The Image Feature Data Learning and the Definition of Image Feature Data Consistency modules adopt the normalization layer of a neural network to extract 3 types of features, namely, global features, feature space, and feature labels. A noise reduction method, Rudin-Osher-Fatemi, is implemented. Thus, the Image Feature Data Association Fusion Heterogeneous Transfer Learning Model is proposed. Also, a correlation coefficient is computed for image feature vectors, and effective correlation mapping matrices are constructed through multi-dimensional vectorized correlation. Then, the feature vectors and correlation coefficients are aggregated using the Batch Normalization Layer to assess correlations between image features. Furthermore, to check the variance between the features of the source images and the target images to be minimum and the common space of the transfer mapping features to be maximum, the Definition of Image Feature Data Consistency deals with controlling parameter separability by designing the constraint matrix with minimum variance score for the source images and the target images. Finally, the regularization of the transfer mapping matrix is carried out to create the loss function to consistently train the image features to construct the heterogeneous transfer learning module. When the transfer learning weight matrix is attained, the consistent constraint strategy of the image features is introduced to update image features in real time. Besides, the Gaussian kernel function is employed to control the generated noise in transfer learning. The results indicate that the SNR is greater than 35dB and the edges in the image feature map are clearer and contain less noise in the Image Feature Data Learning module with the Rudin-Osher-Fatemi denoising strategy.