A coarse-to-fine taxonomy of constellations for fast multi-class object detection
In order for recognition systems to scale to a larger number of object categories building visual class taxonomies is important to achieve running times logarithmic in the number of classes [1, 2]. In this paper we propose a novel approach for speeding up recognition times of multi-class part-based object representations. The main idea is to construct a taxon- omy of constellation models cascaded from coarse-to-ne resolution and use it in recognition with an ecient search strategy. The taxonomy is built automatically in a way to minimize the number of expected compu- tations during recognition by optimizing the cost-to-power ratio . The structure and the depth of the taxonomy is not pre-determined but is inferred from the data. The approach is utilized on the hierarchy-of-parts model  achieving eciency in both, the representation of the structure of objects as well as in the number of modeled object classes. We achieve speed-up even for a small number of object classes on the ETHZ and TUD dataset. On a larger scale, our approach achieves detection time that is logarithmic in the number of classes.