title: Graph matching vs mutual information maximization for object detection.
abstract: Labeled Graph Matching (LGM) has been shown successful in numerous object vision tasks. This method is the basis for arguably the best face recognition system in the world. We present an algorithm for visual pattern recognition that is an extension of LGM ('LGM+'). We compare the performance of LGM and LGM+ algorithms with a state of the art statistical method based on Mutual Information Maximization (MIM). We present an adaptation of the MIM method for multi-dimensional Gabor wavelet features. The three pattern recognition methods were evaluated on an object detection task, using a set of stimuli on which none of the methods had been tested previously. The results indicate that while the performance of the MIM method operating upon Gabor wavelets is superior to the same method operating on pixels and to LGM, it is surpassed by LGM+. LGM+ offers a significant improvement in performance over LGM without losing LGM' s virtues of simplicity, biological plausibility, and a computational cost that is 2-3 orders of magnitude lower than that of the MIM algorithm.
authors.affiliation: California Institute of Technology, Computation and Neural Systems, Division for Biology, Pasadena 92215, USA. ladan@caltech.edu
authors.forename: L B
authors.initials: LB
authors.lastname: Sham
authors.forename: M J
authors.initials: MJ
authors.lastname: Brady
authors.forename: S
authors.initials: S
authors.lastname: Schaal
mesh_terms: D000465:Algorithms; D001330:Electronic Data Processing; D007091:Image Processing, Computer-Assisted; D010363:Pattern Recognition, Automated