%A Feng TIAN, Xin LI, Fang LIU, Chuang LI, Xiaoqiang SUN, Ruishan DU %T A semantictag generation method based on multi-model subspace learning %0 Journal Article %D 2020 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2019.364 %P 31-37 %V 50 %N 3 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1934.shtml} %8 2020-06-20 %X

A multi-model subspace learning semantic tag generation method was proposed, whic was based on the visual space and label space tag correlation modeling method separately. This method reconstructed the "image-tag" correlation in a non-linear manner by establishing a visual feature similarity map, thereby unifying the visual modal representation of the image and the text modal representation of the tag into a multi-model subspace, and ensuring space structure preservation before and after conversion. In this space, the text modal information of the label and the modal information of the visual content of the image were complementary to each other. The semantically related images and labels were mapped to similar sample points in the space, and the semantic label generation problem was then transformed into the nearest label-neighbors retrieval problem. The results showed that the performance of the proposed method was 36.88% on FLICKR-25K data set, and 44.17% on NUS-WIDE data set, which indicated that the proposed method could greatly improve the accuracy of label generation.