On the Object Proposal
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On the Object ProposalPresented by Yao Lu
10-03-2014
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Intro to Object Proposal
• MotivationSliding window based object detection
Iterate over window size, aspect ratio, and location
Image Feature Extraction Classificaiton
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Intro to Object Proposal• Goal • Fast execution• High recall with low # of candidate boxes• Unsupervised/weakly supervised
• Difference with image saliency
Image Feature Extraction ClassificaitonObject
Proposal
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Selective Search• Selective searchK. Van de Sande et al. Segmentation as selective search for object recognition. ICCV 2011.
Merge of multiple segmentation to propose candidate box
1536 boxes = 96.7 recall
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BING• M. Cheng et al. “BING: Binarized normed gradients for
objectnetss estimation at 300fps”, CVPR 2014.• Resize images to different size & aspect ratio• Train an 8x8 template using a linear SVM• Use linear combination to integrate predictions. • Binarize the template to speed-up
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BING
• Pascal VOC 07
• 1000 => 0.95 recall
• Speed
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Geodesic Object Proposal• P. Krahenbuhl and V. Koltun. Geodesic Object Proposals.
ECCV 2014.
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Edge Boxes• C. Lawrence Zitnick and P. Dollar, “Edge Boxes: Locating Object
Proposals from Edges”, ECCV 2014. • # of contours wholly within in a box indicates the objectness• Method
• Edge detection. (m, )• Group edges using connectivity and orientation. Affinity between edge groups:
• Rank wb on sliding window
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Edge Boxes
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Edge Boxes
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Conclusion
• Object proposal greatly enhances object detection efficiency.
• Current methods have very simple intuitions• Selective search• BING• Geodesic object proposal• Edge boxes
• Future goal of object proposal:• Less # of boxes• Higher recall• Faster speed