CMU RI PhD Thesis Proposal: Pairwise Constraints for Matching, Perceptual Grouping and Recognition

Pairwise Constraints for Matching, Perceptual Grouping and Recognition

Marius Leordeanu
Robotics Institute
Carnegie Mellon University

10 March 2008

Abstract:
Object category recognition is a challenging problem in computer vision, which currently receives a growing interest in the field. This problem is almost ill-posed, because there is no formal definition of what constitutes an object category. While people largely agree on common, useful categories, it is still not clear which are the objects' properties that help us group them into such categories. In this thesis we represent the object category models as graphs of features, and focus mainly on the second order relationships between them: pairwise category-dependent (e.g. shape) as well as pairwise perceptual grouping constraints (e.g. geometrical and color based). The main theme of this thesis is that higher order relationships between model features are more important for category recognition than local, first order features. We present several novel algorithms that take full advantage of such pairwise constraints. Firstly, we present our spectral matching algorithm for the Quadratic Assignment Problem (also known as Graph Matching), along with a novel, efficient method for learning the pairwise parameters. Secondly, we present a novel optimization method which can handle nonlinear, complex functions, and present some of its applications in the context of our work. Thirdly, we discuss our object category recognition approach based on shape alone, which uses pairwise geometric constraints only. Next, we explore ways (based on both color and geometry) to establish perceptual grouping relationships between pairs of features, which are category independent. And finally, we talk about how we plan on combining both the category dependent and the perceptual relationships in order to perform object category recognition.

Further Details

A copy of the thesis proposal document can be found at http://www.cs.cmu.edu/~manudanu/marius_proposal.pdf.

Thesis Committee

* Martial Hebert, Chair
* Rahul Sukthankar
* Fernando De la Torre
* David Lowe, University of British Columbia