  The fact that there exists a gap between low-level features and semantic
meanings of images, called the semantic gap, is known for decades. Resolution
of the semantic gap is a long standing problem. The semantic gap problem is
reviewed and a survey on recent efforts in bridging the gap is made in this
work. Most importantly, we claim that the semantic gap is primarily bridged
through supervised learning today. Experiences are drawn from two application
domains to illustrate this point: 1) object detection and 2) metric learning
for content-based image retrieval (CBIR). To begin with, this paper offers a
historical retrospective on supervision, makes a gradual transition to the
modern data-driven methodology and introduces commonly used datasets. Then, it
summarizes various supervision methods to bridge the semantic gap in the
context of object detection and metric learning.
