Knowledge Leverage from Contours to Bounding Boxes: A Concise Approach to Annotation

Jie-Zhi Cheng, Feng-Ju Chang, Kuang-Jui Hsu, Yen-Yu Lin

Abstract: In the class based image segmentation problem, one of the major concerns is to provide large training data for learning complex graphical models. To alleviate the labeling effort, a concise annotation approach working on bounding boxes is introduced. The main idea is to leverage the knowledge learned from a few object contours for the inference of unknown contours in bounding boxes. To this end, we incorporate the bounding box prior into the concept of multiple image segmentations to generate a set of distinctive tight segments, with the condition that at least one tight segment approaching to the true object contour. A good tight segment is then selected via semi-supervised regression, which bears the augmented knowledge transferred from object contours to bounding boxes. The experimental results on the challenging Pascal VOC dataset corroborate that our new annotation method can potentially replace the manual annotations.