SemanGist: A Local Semantic Image Representation

Dong Wang, Xiaobing Liu, Duanpeng Wang, Jianmin Li, Bo Zhang

Abstract: Although various kinds of image features have been proposed, there exists no single optimal feature which can save the effort of all other features for multimedia analysis applications, e.g. image annotation. In this paper, we propose a novel image representation, Semantic Gist (SemanGist), to combine the merit of multiple features automatically. Given a local image patch, SemanGist converts multiple low-level features of the patch into compact prediction scores of a few predefined semantic categories. To this end, a discriminative multi-label boosting algorithm is adopted. This local SemanGist output allows for incorporating semantic spatial context among adjacent patches. For applications like image annotation, this may further reduce possible annotation errors by considering the label compatibility. The same boosting algorithm is applied to the SemanGist representation, together with low-level features, to ensure the label compatibility. Experiments on an image annotation task show that SemanGist not only achieves compact representation but also incorporates spatial context at low run-time computational cost.