CrossModal Retrieval with Deep Learning - WANG Hongzhi; YAN Yu (Journal of Harbin University of Science and Technology)
To improve the efficiency of crossmodal data retrieval under big data, the authors studied the theory of deep learning and proposed a crossmodal data retrieval model on the basis of the artificial convolutional neural network and the perceptron. This model realizes image feature extraction subnet, text feature extraction subnet and hash code learning subnet based on the powerful learning and representation ability of deep learning.The authors propose multilabel similarity measurement method and model training method based on the concepts of conditional entropy and crossentropy. This paper proves that the accuracy of deep retrieval model increases 10% compared to traditional manual feature extraction method Considering the computational efficiency of large data, this paper greatly simplifies the network structure while guaranteeing a certain accuracy.