Automatic object annotation from weakly labeled data with latent structured SVM

In this paper we present an approach to automatic object annotation. We are given a set of positive images which all contain a certain object and our goal is to automatically determine the position of said object in each image. Our approach first applies a heuristic to identify initial bounding boxes based on color and gradient features. This heuristic is based on image and feature statistics. Then, the initial boxes are refined by a latent structured SVM training algorithm which is based on the CCCP training algorithm. We show that our approach outperforms previous work on multiple datasets.