Pyramid match kernel and related techniques

Image analysis – Histogram processing – With pattern recognition or classification

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C382S168000, C382S159000, C382S190000

Reexamination Certificate

active

07949186

ABSTRACT:
A method for classifying or comparing objects includes detecting points of interest within two objects, computing feature descriptors at said points of interest, forming a multi-resolution histogram over feature descriptors for each object and computing a weighted intersection of multi-resolution histogram for each object. An alternative embodiment includes a method for matching objects by defining a plurality of bins for multi-resolution histograms having various levels and a plurality of cluster groups, each group having a center, for each point of interest, calculating a bin index, a bin count and a maximal distance to the bin center and providing a path vector indicative of the bins chosen at each level. Still another embodiment includes a method for matching objects comprising creating a set of feature vectors for each object of interest, mapping each set of feature vectors to a single high-dimensional vector to create an embedding vector and encoding each embedding vector with a binary hash string.

REFERENCES:
patent: 6373979 (2002-04-01), Wang
patent: 6711287 (2004-03-01), Iwasaki
patent: 6782395 (2004-08-01), Labelle
patent: 7328111 (2008-02-01), Porikli
patent: 7627178 (2009-12-01), Suzuki et al.
patent: 2004/0090453 (2004-05-01), Jasinschi et al.
patent: 2004/0228526 (2004-11-01), Lin et al.
patent: 2007/0101268 (2007-05-01), Hua et al.
patent: 2007/0110306 (2007-05-01), Ling et al.
Swain et al. “Color Indexing”, International Journal of Computer Vision, 1991.
Engel, Joachim “The Multiresolution Histogram”, Metrika, 1997, v46, 41-57.
Christian Wallraven, Barbara Caputo, Arnulf Graf, Recognition with Local Features: The Kernel Recipe, Oct. 2003, In Proceedings IEEE International Conference on Computer Vision, Nice, France.
Josef Sivic, Andrew Zisserman, Video Google: A Text Retrieval Approach to Object Matching in Videos, Apr. 2003, pp. 1-8, 2 Volume Set, Computer Society.
Lior Wolf, Amnon Shashua, Learning Over Sets Using Kernel Principal Angels, Oct. 2003, pp. 913-931, Journal of Machine Learning Research 4, Lior Wolf and Amnon Shashua.
John Lafferty, Guy Lebanon, Information Diffusion Kernels, Dec. 2002,, pp. 1-8, In Advances in Neural Information Processing, Vancouver, Canada.
David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, Jan. 5, 2004, pp. 1-28, International Journal of Computer Vision,.
Thomas Gartner, A Survey of Kernels for Structured Data, Jul. 2003, pp. 1-10, Department of Computer Science, University of Bristol, United Kingdom, Department of Computer Science III, University of Bonn, Germany.
R. Fergus, P. Perona, A. Zisserman, Object Class Recognition by Unsupervised Scale-Invariant Learning, 2003, pp. 1-8, University of Oxford, Oxford, United Kingdom, California Institute of Technology, Pasadena, California.
Serge Belongie, Jitendra Malik, Jan Puzicha, Shape Matching and Object Recognition Using Shape Contexts, Aug. 14, 2001, pp. 509-522, In Proceedings IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, No. 24, Apr. 2002.
Risi Kondor, Tony Jebara, A Kernel Between Sets of Vectors, 2003, pp. 1-8, Computer Science Department, Columbia University M.C. New York.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Pyramid match kernel and related techniques does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Pyramid match kernel and related techniques, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Pyramid match kernel and related techniques will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-2665317

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.