Integration of Points of Interest and Regions of Interest

Images consist of different low-level features, such as Points of Interest (POIs) and Regions of Interest (ROIs). A distinction between POIs and ROIs is that the latter ones have intrinsic scale information while the former ones may not have. In this paper, we propose a scheme to integrate these two kinds of image features. The proposed scheme optimizes feature distribution so that the optimized features become more compact. The scheme also assigns scale information to a POI via a stable association between the POI and a certain “nearest” ROI. We test the proposed integration scheme in terms of the repeatability across various imaging transformations. The experimental results demonstrate the effectiveness of the integration scheme.

[1]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[2]  Qi Li,et al.  Interest Points of General Imbalance , 2009, IEEE Transactions on Image Processing.

[3]  Antonio Torralba,et al.  Unsupervised Detection of Regions of Interest Using Iterative Link Analysis , 2009, NIPS.

[4]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[5]  Jean-Michel Morel,et al.  ASIFT: A New Framework for Fully Affine Invariant Image Comparison , 2009, SIAM J. Imaging Sci..

[6]  Tony Lindeberg,et al.  Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention , 1993, International Journal of Computer Vision.

[7]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[9]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[10]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[11]  Tinne Tuytelaars,et al.  Dense interest points , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Qi Li,et al.  Detecting image points of diverse imbalance , 2008, 2008 15th IEEE International Conference on Image Processing.

[13]  Elli Angelopoulou,et al.  Automatic Region-of-Interest Segmentation and Pathology Detection in Magnetically Guided Capsule Endoscopy , 2011, MICCAI.

[14]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[15]  Xavier Binefa,et al.  A Scale Invariant Interest Point Detector for Discriminative Blob Detection , 2009, IbPRIA.

[16]  Christos Faloutsos,et al.  Unsupervised modeling of object categories using link analysis techniques , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Jieping Ye,et al.  Interest point detection using imbalance oriented selection , 2008, Pattern Recognit..

[18]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[19]  Bo Liu,et al.  A quick scale-invariant interest point detecting approach , 2008, Machine Vision and Applications.

[20]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.