Zero sample image classification method and system based on a convolutional neural network and a factor space

The invention provides a zero sample image classification method and system based on a convolutional neural network and a factor space, and the method comprises the steps: building a unified zero-sample classification neural network: firstly, extracting image features in a data set through a classical convolutional neural network, and enabling the image features to serve as the input of the neuralnetwork; the dimensionality of known factors is reduced by using a factor pressure reduction technology, and the known factors and potential factors are embedded into a network to serve as an intermediate layer to jointly determine a final classification result; the network enables image input to final category output. And training a zero sample classification network, and iteratively determiningnetwork model parameters. And identifying the image by using the zero sample classification neural network to finish classification of the zero sample image. According to the method, a convolutionalneural network model is use