Apparatus and method for training classification model and apparatus for classifying with classification model

A method for training a classification model, comprising:

• a local area obtainment step of, with respect to each sample image in a training set, obtaining a predetermined number of predetermined local areas of the sample image, wherein each predetermined local area is a part of a global area of the sample image;
• a feature extraction step of, with respect to each sample image, setting corresponding numbers of feature extraction layers for the global area and each predetermined local area respectively, to extract a global feature of the global area and a local feature of each predetermined local area respectively, wherein the global area and the predetermined number of predetermined local areas share at least one layer of the corresponding numbers of feature extraction layers set with respect to the global area and each predetermined local area respectively, to combine the global feature and each local feature in the at least one shared layer;
• and
• a loss determination step of calculating, with a loss determination layer, a loss function of the sample image based on combined features of each sample image, and training the classification model based on the loss function.

An apparatus for classifying with a classification model, comprising:

• a second local area obtainment unit configured to obtain a predetermined number of predetermined local areas of an image to be classified, wherein each predetermined local area is a part of a global area of the image to be classified;
• a second feature extraction unit configured to extract, with corresponding numbers of feature extraction layers in the classification model which are set for the global area and each predetermined local area, a global feature of the global area and a local feature of each predetermined local area respectively, wherein in the classification model, the global area and the predetermined number of predetermined local areas share at least one layer of the corresponding numbers of feature extraction layers set with respect to the global area and each predetermined local area respectively, to combine the global feature and each local feature in the at least one shared layer;
• and
• a second loss determination unit configured to calculate, with a loss determination layer in the classification model, a loss function of the image to be classified based on combined features of the image to be classified, and to classify the image to be classified based on the loss function.