Computer Science ›› 2016, Vol. 43 ›› Issue (7): 73-76, 94.doi: 10.11896/j.issn.1002-137X.2016.07.012

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Fast Object Recognition Method Based on Objectness

LIU Tao, WU Ze-min, JIANG Qing-zhu, ZENG Ming-yong and PENG Tao-pin   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In order to solve the poor real-time performance in object recognition,a fast object recognition method was proposed based on objectness.First,binarized normed gradients algorithm is used for a test image to get objectness eva-luations.Then,calculating with the objectness evaluations,a candidate bounding box is extracted.And next,deformable part model (DPM) algorithm is used to predict the object with image regions of the box,which can save time on gliding windows searching.Finally,a quickly expansion-shrinking procedure is used to modify the output boxes of DPM,improving accuracy.Experimental results on the challenging PASCAL VOC 2007 database demonstrate that the proposed method outperforms the state-of-art detection models in accuracy and almost twice faster than cascade DPM in instantaneity.

Key words: Computer vision,Object recognition,Objectness,Deformable part model (DPM),Binarized normed gradients (BING)

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