Hierarchical support vector machine based structural classification with fused hierarchies

In this paper, we consider the problem of hierarchical image classification with multiple semantic views of object categories. A novel method is proposed for computing an image-semantic measure by determining the weights for the semantic similarity among the concepts of each view. After obtaining the new image-semantic measure, we construct a semantic hierarchy with the existing method called TRUST-ME. For the hierarchical classification, we translate the classification task with a learned taxonomy into a structured support vector machine (SVM) learning framework. We demonstrate our method on VOC2010 and a subset of the Animals with Attributes dataset, and show that the structured SVM using the weighted semantic hierarchy provides better accuracy.