The invention provides a graded
image retrieval method based on deep features of a
convolutional neural network. According to the basic principle, the method comprises the steps of firstly, training the
convolutional neural network used for feature extracting, and determining network parameters; then using the trained
convolutional neural network to extract image features, and obtaining multiple convolutional layer binary
system features and one full-joint layer binary
system feature; secondly, applying the convolutional layer binary
system features to a preliminary screening retrieval stage,conducting multi-feature similarity fusion after further compressing the features, sifting out a
candidate image set, and reducing the retrieval range; finally, using the full-joint layer binary system feature to accurately retrieve the
candidate image set to obtain a final
retrieval result. As is shown by an experiment result based on a public
image retrieval dataset, compared with an existing
image retrieval method, the representing mode of the images of the graded image retrieval method is more comprehensive, a feature
compression method is simpler and more efficient, and the retrieval accuracy is high; meanwhile, by means of a graded retrieval mode, the system calculation amount is dispersed, parallel accelerating is achieved, and the graded image retrieval method has practical value.