Image Annotation based on Deep Hierarchical Context Networks
This work aims to improve image annotation accuracy for computer vision researchers by better incorporating contextual information, which is an incremental improvement to existing context modeling techniques.
This paper addresses the problem of image annotation by developing a Deep Hierarchical Context Network (DHCN) that integrates geometric and semantic contextual relationships. The method is evaluated on the ImageCLEF benchmark for image annotation.
Context modeling is one of the most fertile subfields of visual recognition which aims at designing discriminant image representations while incorporating their intrinsic and extrinsic relationships. However, the potential of context modeling is currently underexplored and most of the existing solutions are either context-free or restricted to simple handcrafted geometric relationships. We introduce in this paper DHCN: a novel Deep Hierarchical Context Network that leverages different sources of contexts including geometric and semantic relationships. The proposed method is based on the minimization of an objective function mixing a fidelity term, a context criterion and a regularizer. The solution of this objective function defines the architecture of a bi-level hierarchical context network; the first level of this network captures scene geometry while the second one corresponds to semantic relationships. We solve this representation learning problem by training its underlying deep network whose parameters correspond to the most influencing bi-level contextual relationships and we evaluate its performances on image annotation using the challenging ImageCLEF benchmark.