A cross-media search method. A VGG convolutional neural network (VGG net) is utilized to extract image features, the 4096-dimensional features of a seventh fully-connected layer (fc7) in the VGG net, after processing by a ReLU activation function, serving as image features; a Fisher Vector based on Word2vec is utilized to extract text features; semantic matching is performed on heterogeneous images and the text features by means of logistic regression; a correlation between the two heterogeneous features which are image and text is found by means of semantic matching based on logistic regression, and thus cross-media search is achieved. The feature extraction method can effectively indicate deep semantics of image and text, can improve cross-media search accuracy, and thus greatly improve a cross-media search effect.
CROSS-MEDIA SEARCH METHOD