Comment Location:
http://pacocomputer.blogspot.com/2010/11/reading-15-image-based-trainable-symbol.html
Summary:
The author proposes a trainable, hand-drawn symbol recognizer based on a multi-layer recognition scheme. Binary templates are used to represent the symbols. The author uses multiple classifiers to rank a symbol and thus increase the overall accuracy of the system. The 4 classifiers are Hausdorff Distance, Modified Hausdorff Distance, Tanimoto Coefficient, and Yule Coefficient.
The author discovered limitations among his shape set when he tried to compare sketches that had shapes (like arrows) differing mainly by direction, size, or some other small detail.
Discussion:
The author realized there is currently no perfect algorithm in sketch recognition. The idea to employ multiple recognizers is a step forward in progress. It also increases the coding, but then again, nothing's perfect. Maybe if the author slapped on a few more classifiers and weighted their input, the overall recognition of the symbol would increase.
When you can not decide, the best idea is find several friends to help you make a decision. Hopefully at least one friend can give a correct answer.
ReplyDeleteNothing perfect is like no free lunch. There are always exceptions for each classifier. So to combine a few of classifiers may be a good idea.
However, remember that more classifiers means more computation time, which this paper is trying to avoid. Therefore, we must intelligently select which friends will help us answer the question ;)
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