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On the Object Proposal

On the Object Proposal. Presented by Yao Lu 10-03-2014. Intro to Object Proposal. Motivation Sliding window based object detection Iterate over window size, aspect ratio, and location. Intro to Object Proposal. Goal Fast execution High recall with low # of candidate boxes

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On the Object Proposal

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  1. On the Object Proposal Presented by Yao Lu 10-03-2014

  2. Intro to Object Proposal • Motivation Sliding window based object detection Iterate over window size, aspect ratio, and location

  3. Intro to Object Proposal • Goal • Fast execution • High recall with low # of candidate boxes • Unsupervised/weakly supervised • Difference with image saliency

  4. Selective Search • Selective search K. 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

  5. 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

  6. BING • Pascal VOC 07 • 1000 =>0.95 recall • Speed

  7. Geodesic Object Proposal • P. Krahenbuhl and V. Koltun. Geodesic Object Proposals. ECCV 2014.

  8. 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

  9. Edge Boxes

  10. Edge Boxes

  11. 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

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