  Current top performing object recognition systems build on object proposals
as a preprocessing step. Object proposal algorithms are designed to generate
candidate regions for generic objects, yet current approaches are limited in
capturing the vast variety of object characteristics. In this paper we analyze
the error modes of the state-of-the-art Selective Search object proposal
algorithm and suggest extensions to broaden its feature diversity in order to
mitigate its error modes. We devise an edge grouping algorithm for handling
objects without clear boundaries. To further enhance diversity, we incorporate
the Edge Boxes proposal algorithm, which is based on fundamentally different
principles than Selective Search. The combination of segmentations and edges
provides rich image information and feature diversity which is essential for
obtaining high quality object proposals for generic objects. For a preset
amount of object proposals we achieve considerably better results by using our
combination of different strategies than using any single strategy alone.
