Selective Search for Object Recognition

Int J Comput VisDOI Search for Object RecognitionJ. R. R. Uijlings K. E. A. van de Sande T. Gevers A. W. M. SmeuldersReceived: 5 May 2012 / Accepted: 11 March 2013 Springer Science+Business Media New York 2013 AbstractThis paper addresses the problem of generatingpossible Object locations for use in Object Recognition . Weintroduce Selective Search which combines the strength ofboth an exhaustive Search and segmentation. Like segmen-tation, we use the image structure to guide our samplingprocess. Like exhaustive Search , we aim to capture all possi-ble Object locations. Instead of a single technique to generatepossible Object locations, we diversify our Search and use avariety of complementary image partitionings to deal withas many image conditions as possible.

2.1 Exhaustive Search As an object can be located at any position and scale in the image, it is natural to search everywhere ( Dalal and Triggs 2005 ;Harzallah et al. 2009 Viola and Jones 2004). How-ever, the visual search space is huge, making an exhaustive search computationally expensive. This imposes constraints

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