Many-to-Many Feature Matching in Object Recognition

One of the bottlenecks of current recognition (and graph matching) systems is their assumption of one-to-one feature (node) correspondence. This assumption breaks down in the generic object recognition task where, for example, a collection of features at one scale (in one image) may correspond to a single feature at a coarser scale (in the second image). Generic object recognition therefore requires the ability to match features many-to-many. In this paper, we will review our progress on three independent object recognition problems, each formulated as a graph matching problem and each solving the many-to-many matching problem in a different way. First, we explore the problem of learning a 2-D shape class prototype (represented as a graph) from a set of object exemplars (also represented as graphs) belonging to the class, in which there may be no one-to-one correspondence among extracted features. Next, we define a low-dimensional, spectral encoding of graph structure and use it to mat...

• Type: Conference
• Year: 2006
• Where: DAGSTUHL
• Authors: Ali Shokoufandeh, Yakov Keselman, M. Fatih Demirci, Diego Macrini, Sven J. Dickinson
• Keywords: DAGSTUHL 2006, DAGSTUHL 2007, Graph Matching, Matching Problem, Object Recognition