Francisco
Summary:
The paper propose a recognition method based on Bayesian conditional random fields (BCRFs), which jointly analyzes all drawing elements. The first half of the paper is to introduce how BCRFs works and how ARD (Automatic Relevance Determination) incorporates into BCRFs. Honestly speaking, the mathmatics is too hard to understand for me. Then it introduces the application to ink classification. First, subdivision of pen strokes in fragments, just like PaeloRecognizer. Second, construction of a conditional random field on the fragments. Third, training and inference on the network using BCRFs. The experiments show the efficiency of BCRF-ARD, though it costs longer time.
Discussion:
The algorithm proposed in the paper seems good, because it incorporate two good algorihtms into a better one. The experiments show that it is more efficient that other algorithms.
However, generally speaking, I cannot really understand how to apply BCRFs into sketch recognition. So the discussion may be biased. After reading, I would like to say the author writes the paper in order to write. I think such a complex method is used to solve a binary classification (container or connector). Can it be realized by PaleoRecognizer, just to find the rectangle?
An ink feature, really sounds impossible to me.
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