Graph matching vs mutual information maximization for object detection

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...

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Bibliographic Details
Published in:Neural networks Vol. 14; no. 3; pp. 345 - 354
Main Authors: Shams, Ladan B., Brady, Mark J., Schaal, Stefan
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.04.2001
Elsevier Science
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Automatic Data Processing
Computer science; control theory; systems
Exact sciences and technology
Gabor wavelets
Graph matching
Image entropy
Image Processing, Computer-Assisted
Lateral excitation
Mutual information maximization
Object detection
Object recognition
Pattern recognition
Pattern Recognition, Automated
Pattern recognition. Digital image processing. Computational geometry
Image entropy
Mutual information maximization
Lateral excitation
Object detection
Pattern recognition
Object recognition
Gabor wavelets
Graph matching
Labelled graph
Statistical method
Algorithm performance
Comparative study
Mutual information
ISSN:0893-6080, 1879-2782
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0893-6080
1879-2782
DOI:10.1016/S0893-6080(00)00099-X