Unsupervised multi-view object proposal ranking

This paper proposes a novel region-based structure measure for object proposal ranking. It is able to efficiently reduce redundant object proposals and highlight dominant objects in an image. The computation of this measure is fully unsupervised, without any image level annotation or any visual semantics labeling. In this work, a new set of heuristic rules are introduced to indicate regions that may contain objects. The distinctiveness of a proposal region is assessed based on its structural uniqueness, structural distributions and deformable shapes. A scoring function is then constructed to combine these multi-view rule-based assessments into a single object score. Furthermore, a rank-recall optimization is proposed to optimize the scoring function for proposal ranking. The final optimized ranking significantly reduces the number of object proposals while maintaining potential object regions. Promising results show that the proposed ranking method simultaneously reduces proposals and highlights dominant objects.

[1]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[2]  Chong Wang,et al.  Weakly Supervised Object Localization with Latent Category Learning , 2014, ECCV.

[3]  George R. Thoma,et al.  Edge map analysis in chest X-rays for automatic pulmonary abnormality screening , 2016, International Journal of Computer Assisted Radiology and Surgery.

[4]  Tao Xiang,et al.  Weakly supervised object detector learning with model drift detection , 2011, 2011 International Conference on Computer Vision.

[5]  Svetlana Lazebnik,et al.  Scene recognition and weakly supervised object localization with deformable part-based models , 2011, 2011 International Conference on Computer Vision.

[6]  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.

[7]  Fei-Fei Li,et al.  Efficient Image and Video Co-localization with Frank-Wolfe Algorithm , 2014, ECCV.

[8]  Yael Pritch,et al.  Saliency filters: Contrast based filtering for salient region detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Tao Xiang,et al.  Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation , 2013, 2013 IEEE International Conference on Computer Vision.

[10]  Cordelia Schmid,et al.  Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Jean Ponce,et al.  Unsupervised Object Discovery and Tracking in Video Collections , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[12]  David A. McAllester,et al.  Object Detection with Grammar Models , 2011, NIPS.

[13]  Fatih Murat Porikli,et al.  Saliency-aware geodesic video object segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[15]  Tao Xiang,et al.  In Defence of Negative Mining for Annotating Weakly Labelled Data , 2012, ECCV.

[16]  Cordelia Schmid,et al.  Multi-fold MIL Training for Weakly Supervised Object Localization , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Ce Liu,et al.  Unsupervised Joint Object Discovery and Segmentation in Internet Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[19]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[21]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

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

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