Transcription of Selective Search for Object Recognition
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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. Our Selective searchresults in a small set of data-driven, class-independent, highquality locations, yielding 99% recall and a Mean AverageBest Overlap of at 10,097 locations.
Int J Comput Vis Fig. 2 Two examples of our selective search showing the necessity of different scales. On the left we find many objects at different scales. On the right we necessarily find the objects at different scales as the girl is contained by the tv 3 Selective Search In this section we detail our selective search algorithm for object recognition and present a variety of diversification
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