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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Vydané v:	Neural networks Ročník 14; číslo 3; s. 345 - 354
Hlavní autori:	Shams, Ladan B., Brady, Mark J., Schaal, Stefan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Elsevier Ltd 01.04.2001 Elsevier Science
Predmet:	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
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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.
AbstractList	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.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. 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. 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.
Author	Brady, Mark J. Shams, Ladan B. Schaal, Stefan
Author_xml	– sequence: 1 givenname: Ladan B. surname: Shams fullname: Shams, Ladan B. email: ladan@caltech.edu organization: California Institute of Technology, Computation and Neural Systems, Division for Biology, MC 139-74, Pasadena, CA 92215, USA – sequence: 2 givenname: Mark J. surname: Brady fullname: Brady, Mark J. organization: 3M Corporate Research Laboratories, 3M Center, Building 235-3F-08, St. Paul, MN 55144, USA – sequence: 3 givenname: Stefan surname: Schaal fullname: Schaal, Stefan organization: University of Southern California, Computer Science & Neuroscience, HNB 103, Los Angeles, CA 90089-2520, USA
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Snippet	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...
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SubjectTerms	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
Title	Graph matching vs mutual information maximization for object detection
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