  A multi-scale greedy-based object proposal generation approach is presented.
Based on the multi-scale nature of objects in images, our approach is built on
top of a hierarchical segmentation. We first identify the representative and
diverse exemplar clusters within each scale by using a diversity ranking
algorithm. Object proposals are obtained by selecting a subset from the
multi-scale segment pool via maximizing a submodular objective function, which
consists of a weighted coverage term, a single-scale diversity term and a
multi-scale reward term. The weighted coverage term forces the selected set of
object proposals to be representative and compact; the single-scale diversity
term encourages choosing segments from different exemplar clusters so that they
will cover as many object patterns as possible; the multi-scale reward term
encourages the selected proposals to be discriminative and selected from
multiple layers generated by the hierarchical image segmentation. The
experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012
segmentation dataset demonstrate the accuracy and efficiency of our object
proposal model. Additionally, we validate our object proposals in simultaneous
segmentation and detection and outperform the state-of-art performance.
