Multi-label classification of traditional national costume pattern image semantic understanding

Since current image multi-label classification methods only focus on the category information of image ontology (ontology) and ignore the deep semantic information of the image (implicit), this study proposed an image multi-label classification model of "ontology-implicit" fusion learning. The model first used the middle and higher layers of CNN to learn the image ontology information and implicit information, respectively, and then it used the dependency relationship between the ontology information and implicit information to design the fusion learning model. Meanwhile, the different characteristics of the middle layer and different structures of the model were studied in-depth, to realize the classification of implicit information contained in multiple image categories. Experiments conducted on the traditional national costume pattern image datasets show that the mAP of image ontology multi-label classification and implicit multi-label classification are 0.88 and 0.82, respectively. Comparative experiments conducted on the Scene dataset show that the model is superior to other methods in Hamming loss, one error, and average precision indices, with values of 0.103, 0.091, and 0.083, respectively. Therefore, the experimental results prove the effectiveness and superiority of this method. © 2020, Science Press. All right reserved.