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Graph-matching vs. entropy-based methods for object detection




Labeled Graph Matching (LGM) has been shown successful in numerous ob-ject 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 LGMâ??s virtues of simplicity, biological plausibility, and a computational cost that is 2-3 orders of magnitude lower than that of the MIM algorithm. 

Book Title: Neural Networks
Volume: 14
Number (issue): 3
Pages: 345-354
Year: 2001

Department(s): Autonome Motorik
Bibtex Type: Article (article)

Cross Ref: p1267
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/shams-NN2001.pdf


  title = {Graph-matching vs. entropy-based methods for object detection},
  booktitle = {Neural Networks},
  volume = {14},
  number = {3},
  pages = {345-354},
  year = {2001},
  note = {clmc},
  crossref = {p1267},
  url = {http://www-clmc.usc.edu/publications/S/shams-NN2001.pdf}