Snapscreen: TV-stream frame search with projectively distorted and noisy query

In this work we describe an approach to real-time image search in large databases robust to variety of query distortions such as lighting alterations, projective distortions or digital noise. The approach is based on the extraction of keypoints and their descriptors, random hierarchical clustering trees for preliminary search and RANSAC for refining search and result scoring. The algorithm is implemented in Snapscreen system which allows determining a TV-channel and a TV-show from a picture acquired with mobile device. The implementation is enhanced using preceding localization of screen region. Results for the real-world data with different modifications of the system are presented.

[1]  Yannis Avrithis,et al.  Hough Pyramid Matching: Speeded-Up Geometry Re-ranking for Large Scale Image Retrieval , 2014, International Journal of Computer Vision.

[2]  Leon Cruickshank,et al.  Interacting with Digital Media at Home via a Second Screen , 2007, Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007).

[3]  BentleyJon Louis Multidimensional binary search trees used for associative searching , 1975 .

[4]  Pascal Fua,et al.  Receptive Fields Selection for Binary Feature Description , 2014, IEEE Transactions on Image Processing.

[5]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[6]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[7]  Mubarak Shah,et al.  Complex Events Detection Using Data-Driven Concepts , 2012, ECCV.

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

[9]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  David J. Fleet,et al.  Fast search in Hamming space with multi-index hashing , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Alexandr Andoni,et al.  Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[12]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[13]  P. Fua,et al.  Towards Recognizing Feature Points using Classification Trees , 2004 .

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

[15]  Dmitry P. Nikolaev,et al.  Real time rectangular document detection on mobile devices , 2015, Other Conferences.

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

[18]  John Langford,et al.  Cover trees for nearest neighbor , 2006, ICML.

[19]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.