Cross-domain small sample image classification model method focusing on fine-grained recognition
A classification model, small sample technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve problems such as fine-grained image recognition
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[0064] Below in conjunction with accompanying drawing, further describe the present invention through embodiment, but do not limit the scope of the present invention in any way.
[0065] The model structure is as figure 1 shown. The method of the present invention includes: 1) building a cross-domain small-sample image classification model focusing on fine-grained recognition: the model extracts image features through a front-end dedicated feature encoder, and implements classification and recognition using image features in the back-end bilinear metric function; 2) Pre-training Focused Feature Encoder (MFFE): Pre-training image classification and recognition on the mini-ImageNet data set, and then transferring the pre-trained MFFE model and parameters to the FFGR model of the present invention and combining with BMF, As the front end of FFGR, it is used to extract the feature information of the image; 3) Focus on fine-grained recognition (FFGR) model classification and recog...
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