Image classification method and device

Abstract

The invention discloses an image classification method and an image classification device. The image classification method comprises the following steps: based on a big image data set, training an AlexNet model structure; migrating the five trained convolution layers to a small database to form a lower-level feature extraction layer, and constructing together with a residual network layer, a multiscale pooling layer, a feature layer and a softmax classifier to obtain a migration model structure, wherein the residual network layer includes two convolution layers; inputting small image data set into the migration model structure, upgrading parameters by adopting a batch gradient descending method, and training an image classification hybrid model; and classifying according to the image classification hybrid model to obtain a classification result. By migrating the pretrained convolution layers on the big data set to the small data set, increasing the multiscale pooling layer, and serially connecting the feature quantity output by the residual network layer and the multiscale pooling layer and inputting to the classifier, the feature quantity is increased and the overfitting problem can be relieved; and through the hybrid model trained based on a convolution nerve network and a migration learning, the image classification accuracy can be effectively improved.

Description

technical field

[0001] The present invention relates to the field of transfer learning and deep learning, in particular to an image classification method and device. Background technique

[0002] Convolutional Neural Networks (CNN) is an efficient recognition method. Generally, the basic structure of CNN includes two layers, one is the feature extraction layer, the input of each neuron is connected to the local receptive field of the previous layer, and the local features are extracted. Once the local feature is extracted, the positional relationship between it and other features is also determined; the second is the feature map layer, each calculation layer of the network is composed of multiple feature maps, each feature map is a plane, All neurons on the plane have equal weights.

[0003] Transfer learning is the influence of one kind of learning on another kind of learning, which widely exists in the learning of knowledge, skills, attitudes and behavioral norms. Any k...

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