Integrating multiple model views for object recognition

V. Ferrari, T. Tuytelaars, L. Van Gool

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We present a new approach to appearance-based object recognition, which captures the relationships between multiple model views and exploits them to improve recognition performance. The basic building block is local, viewpoint invariant regions. We propose an efficient algorithm for partitioning a set of region matches into groups lying on smooth surfaces (GAMs). During modeling, the model views are connected by a large number of region-tracks, each aggregating image regions of a single physical region across the views. At recognition time, GAMs are constructed matching a test image to each model view. The consistency of configurations of GAMs is measured by exploiting the model connections. A genetic algorithm finds covering the object as completely as possible the most consistent configuration. Introducing GAMs as an intermediate grouping level facilitates decision-making and improves discriminative power. As a complementary application, we introduce a novel GAM-based two-view filter and demonstrate its effectiveness in recovering correct matches in the presence of up to 96% mismatches.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)0-7695-2158-4
Publication statusPublished - 1 Jun 2004


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