Comments
Jonathan Hall
Summary
The paper discussed an algorithm of image-based symbol recognition for hand-drawn sketches.
The key idea is very similar to "one dollar", which collectes training sets from users and directly add them into template sets. However, the paper gives some better idea in recognition process.
Processing and Representation
The symbol is first compressed to 48*48 by quantization. Using polar coordinate instead of x-y coordinate to deal with rotation. The rotation in x-y coordinate is the translation in polar coordinate.
Pre-Recognizer
Polar transform can also be used as a pre-recognizer, which can filt out those impossible samples. It reduces a lot of computation time.
Mutliple classifiers
User four kinds of distance metrics, Hausdorff distance, Modified Hausdorff distance, Tanimoto coefficient, Yule coefficient. Each classifier make a decision, and combine four decisions to make a final decision. It can reduce inaccuracy caused by only one classifier.
User Study
The paper conducts two user studies to verify its algorithm, graphic symbol and digits. Both these two studies support its algorithm very well.
However, this algorithm is also limited in several aspects. It is sensitive to non-uniform scaling, and may wash out small image details during quantization.
Discussion
Excellent idea in employing four classifiers and combine results to give a final result. Sometimes, we don't know which classifier is good or fit for the current problem, or which classifier is good or fit for which samples. So using four classifier can really reduce the inaccuracy which usually occurs in only one classifier. I think it is a very valuable idea for me, and give me some inspiration in my final project.
However, the disadvantage is also very obvious as one dollar is. The number of templates increases very fast, because it must collect as many samples as it can. This is the main disadvantage that all "one dollar" systems have.
I guess this is something similar to what Josh mentioned in his research presentation. Using ensemble classifiers for COA sketch recognition......I cant remember the exam term he mentioned, something like meta search may be?
回复删除After seeing your final presentation, it was obvious that this paper had some impact on your group's project. It is good that your project had users draw multiple instances of examples shapes at the same time.
回复删除It also different with $1 in dealing with rotations. In this paper, the author rotate the templates to match with the candidate, while in $1, the candidates are rotated, not the templates.
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