In this work we propose an object detection approach that extracts a limited number of candidate local regions to guide the detection process. The basic idea of the approach is that object location can be determined by clustering points of interest and hierarchically forming candidate regions according to similarity and spatial proximity predicates. Statistical validation shows that the method is robust across a substantial range of content diversity while its response seems to be comparable to other state of the art object detectors.