Semantic Hierarchies for Visual Object Recognition

Marcin Marszalek, Cordelia Schmid

Abstract: In this paper we propose to use lexical semantic networks to extend the state-of-the-art object recognition techniques. We use the semantics of image labels to integrate prior knowledge about inter-class relationships into the visual appearance learning. We show how to build and train a semantic hierarchy of discriminative classifiers and how to use it to perform object detection. We evaluate how our approach influences the classification accuracy and speed on the PASCAL VOC challenge 2006 dataset, a set of challenging real-world images. We also demonstrate additional features that become available to object recognition due to the extension with semantic inference toolswe can classify high-level categories, such as animals, and we can train part detectors, for example a window detector, by pure inference in the semantic network.