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A novel efficient method for extraction of object proposals is introduced.Its "objectness" function exploits deep spatial pyramid features, a novelfast-to-compute HoG-based edge statistic and the EdgeBoxes score. Theefficiency is achieved by the use of spatial bins in a novel combination withsparsity-inducing group normalized SVM. State-of-the-art recall performance isachieved on Pascal VOC07, significantly outperforming methods with comparablespeed. Interestingly, when only 100 proposals per image are considered themethod attains 78% recall on VOC07. The method improves mAP of the RCNNstate-of-the-art class-specific detector, increasing it by 10 points when only50 proposals are used in each image. The system trained on twenty classesperforms well on the two hundred class ILSVRC2013 set confirming generalizationcapability.

Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection