Label Enhancement Manifold Learning Algorithm for Multi-label Image Classification
Chao Tan, Genlin Ji · 2020
In this paper, we propose a new label enhancement manifold learning algorithm to solve the multi-label image classification problem. Predicting multiple objects in a traffic video image is a challenging problem. Our idea is to use label distribution learning (LDL) to enrich the label space and improve label recognition in the original label space. We use the feature function representing the manifold structure to guide the geometric meaning of the label space and transform the local topology from the feature space to the label space. We first build a tag distribution learner. Next, use the LDL model for classification. The similarity between the two distributions is measured by Bayesian divergence, and the label distribution is learned through the maximum entropy model and the objective function of this paper is established. Finally, an enhanced label model of the manifold space is established to reduce the dimensionality of the feature matrix generated during the training phase, so that the supervised information in the label manifold can be used in the incremental manifold space to improve the accuracy of feature extraction. Compared to the latest multi-label learning methods, our label enhancement model has advantages in predicting performance.