Automatic object detection using objectness measure

Object detection is an important step towards object recognition. A robust object detection system is one that can detect an object of any class. In this paper, we present a fully automatic approach to object detection based on an objectness measure. The proposed automatic object detection approach quantifies the likelihood for an image window to encompass objects in the image. It can discriminate between multiple objects in a scene, with individual windows capturing each detected object. Most importantly, the proposed approach does not require any manual input. We tested this approach on the challenging PASCAL VOC 07 dataset. Experimental results show that our approach provides a more accurate estimation of the required number of windows for an input image. The proposed technique is computationally efficient and takes less than 4 sec. per image.