A Bottom-Up Approach for Learning Visual Object Detection Models from Unreliable Sources

Abstract

The ability to learn models of computational vision from sample data has significantly advanced the field. Obtaining suitable training image sets, however, remains a challenging problem. In this paper we propose a bottom-up approach for learning object detection models from weakly annotated samples, i.e., only category labels are given per image. By combining visual saliency and distinctiveness of local image features regions of interest are extracted in a completely automatic way without requiring detailed annotations. Using a bag-of-features representation of these regions, object recognition models can be trained for the given object categories. As weakly labeled sample images can easily be obtained from image search engines, our approach does not require any manual annotation effort. Experiments on data from the Visual Object Classes Challenge 2011 show that promising object detection results can be achieved by our proposed method.