Abstract
Extended from the traditional pure statistical learning methods, we propose to augment the statistical learning methods with ontology and apply this idea for image attribute learning. In order to capture structural information among attributes, the graph-guided fused lasso model is adopted and improved by a new distance metric based on WordNet. The novelty of our method is that we find the semantic correlation with the ontology-guided attribute space and integrate inter-attribute similarity information into the learning model. The hierarchy of ImageNet is exploited to define the image attributes and a dataset from ImageNet including over 30,000 images is collected. The experimental results show that this method can both improve the accuracy and accelerate the algorithm convergency. Moreover, the learned semantic correlation owns transfer ability to related applications.