Sequential Optimization for Efficient High-Quality Object Proposal Generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.

[1]  Rynson W. H. Lau,et al.  Oriented Object Proposals , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Vladlen Koltun,et al.  Geodesic Object Proposals , 2014, ECCV.

[4]  Vladlen Koltun,et al.  Learning to propose objects , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  H. Teuber.  Physiological psychology. , 1955, Annual review of psychology.

[7]  Tao Xiang,et al.  Making better use of edges via perceptual grouping , 2015, CVPR.

[8]  Philip H. S. Torr,et al.  BING: Binarized normed gradients for objectness estimation at 300fps , 2014, Computational Visual Media.

[9]  Luc Van Gool,et al.  DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  James M. Rehg,et al.  RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Liqing Zhang,et al.  Object proposal by multi-branch hierarchical segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Daniel Tarlow,et al.  Optimizing Expected Intersection-Over-Union with Candidate-Constrained CRFs , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Derek Hoiem,et al.  Category-Independent Object Proposals with Diverse Ranking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Baolin Yin,et al.  Cracking BING and Beyond , 2014, BMVC.

[15]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.

[16]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[17]  Santiago Manen,et al.  Prime Object Proposals with Randomized Prim's Algorithm , 2013, 2013 IEEE International Conference on Computer Vision.

[18]  Hayit Greenspan,et al.  Finding Pictures of Objects in Large Collections of Images , 1996, Object Representation in Computer Vision.

[19]  Shih-Fu Chang,et al.  An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[20]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[21]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Bernt Schiele,et al.  How good are detection proposals, really? , 2014, BMVC.

[23]  Ming-Hsuan Yang,et al.  Weakly Supervised Object Localization with Progressive Domain Adaptation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  James M. Rehg,et al.  The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[25]  Huimin Ma,et al.  Improving object proposals with multi-thresholding straddling expansion , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Sven J. Dickinson,et al.  Learning to Combine Mid-Level Cues for Object Proposal Generation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[27]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[28]  Philip H. S. Torr,et al.  Approximate structured output learning for Constrained Local Models with application to real-time facial feature detection and tracking on low-power devices , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[29]  Philip H. S. Torr,et al.  Object Proposal Generation Using Two-Stage Cascade SVMs , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Cewu Lu,et al.  Contour Box: Rejecting Object Proposals without Explicit Closed Contours , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[31]  Cristian Sminchisescu,et al.  CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Daniel P. Huttenlocher,et al.  Distance Transforms of Sampled Functions , 2012, Theory Comput..

[33]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Bernt Schiele,et al.  What Makes for Effective Detection Proposals? , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Ken Kelley,et al.  Group membership prediction when known groups consist of unknown subgroups: a Monte Carlo comparison of methods , 2014, Front. Psychol..

[36]  C. Lawrence Zitnick,et al.  Structured Forests for Fast Edge Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[37]  Esa Rahtu,et al.  Generating Object Segmentation Proposals Using Global and Local Search , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Zhuowen Tu,et al.  Supervised Learning of Edges and Object Boundaries , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[39]  Cewu Lu,et al.  Complexity-adaptive distance metric for object proposals generation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[41]  William J. Cook,et al.  Combinatorial optimization , 1997 .

[42]  Ian D. Reid,et al.  gSLICr: SLIC superpixels at over 250Hz , 2015, ArXiv.

[43]  Philip H. S. Torr,et al.  Efficient online structured output learning for keypoint-based object tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Matthew B. Blaschko,et al.  Non Maximal Suppression in Cascaded Ranking Models , 2013, SCIA.

[45]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[46]  Venkatesh Saligrama,et al.  Efficient Activity Retrieval through Semantic Graph Queries , 2015, ACM Multimedia.

[47]  Jonathon Shlens,et al.  Fast, Accurate Detection of 100,000 Object Classes on a Single Machine , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[48]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[51]  Ziming Zhang,et al.  Efficient object detection via structured learning and local classifiers , 2013 .

[52]  Francis Eng Hock Tay,et al.  Scale-Aware Pixelwise Object Proposal Networks , 2016, IEEE Transactions on Image Processing.

[53]  Venkatesh Saligrama,et al.  A Novel Visual Word Co-occurrence Model for Person Re-identification , 2014, ECCV Workshops.

[54]  Jonathan T. Barron,et al.  Multiscale Combinatorial Grouping , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[55]  Nicu Sebe,et al.  Learning to Group Objects , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[56]  Jonathan Warrell,et al.  Proposal generation for object detection using cascaded ranking SVMs , 2011, CVPR 2011.

[57]  Matthew B. Blaschko,et al.  Learning a category independent object detection cascade , 2011, 2011 International Conference on Computer Vision.

[58]  Sebastian Nowozin,et al.  Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.