Simultaneous detection and segmentation for generic objects

Numerous approaches to object detection and segmentation have been proposed so far. However, these methods are prone to fail in some general situations due to the proper object nature. For instance, classical approaches of object detection and segmentation obtain good results for some specific object classes (i.e. detection of pedestrians or segmentation of cars). However, these methods have troubles when detecting or segmenting object classes with different distinctive characteristics (i.e. cars and horses versus sky and road). In this paper, we propose a general framework to simultaneously perform object detection and segmentation on objects of different nature. Our approach is based on a boosting procedure which automatically decides - according to the object properties - whether is better to give more weight to the detection or segmentation process to improve both results. We validate our approach using different object classes from La-belMe, TUD and Weizmann databases, obtaining competitive detection and segmentation results.