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🔗 Source: robotics.stanford.edu
📊 Size: 470 KB
📄 Pages: 8 pages
⬇️ Downloads: 314

Summary

Graph matching is a fundamental problem in pattern recognition, where patterns are modeled as graphs and recognition involves finding a correspondence between nodes of different graphs. The problem is typically formulated as a quadratic assignment problem, which is NP-hard. Most research focuses on designing efficient algorithms to solve this problem approximately. This paper addresses the complementary problem of estimating compatibility functions to improve graph matching performance. A method for learning graph matching is presented, where training examples are pairs of graphs with manually provided matchings. Experimental results show that learning improves the performance of standard graph matching algorithms, and linear assignment with learning can outperform state-of-the-art quadratic assignment relaxation algorithms. This suggests that learning can enable speed-ups of graph matching by up to 4 orders of magnitude while retaining state-of-the-art accuracy.

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Graph matching is a fundamental problem in pattern recognition, where patterns are modeled as graphs and recognition involves finding a correspondence between...

Technical Information

  • File Format: PDF
  • File Size: 470 KB
  • Pages: 8
  • Language: EN
  • Total Downloads: 314
  • Last Updated: 2 weeks ago

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