Beyond Bounding-Boxes: Learning Object Shape by Model-Driven Grouping

Visual recognition requires to learn object models from training data. Commonly, training samples are annotated by marking only the bounding-box of objects, since this appears to be the best trade-off between labeling information and effectiveness. However, objects are typically not box-shaped. Thus, the usual parametrization of object hypotheses by only their location, scale and aspect ratio seems inappropriate since the box contains a significant amount of background clutter. Most important, however, is that object shape becomes only explicit once objects are segregated from the background. Segmentation is an ill-posed problem and so we propose an approach for learning object models for detection while, simultaneously, learning to segregate objects from clutter and extracting their overall shape. For this purpose, we exclusively use bounding-box annotated training data. The approach groups fragmented object regions using the Multiple Instance Learning (MIL) framework to obtain a meaningful representation of object shape which, at the same time, crops away distracting background clutter to improve the appearance representation.