Comments:
Jonathan
Summary:
The recognition method discussed in this paper is based on Bayesian conditional random fields (BCRFs). BCRFs consider both spatial and temporal information, and can correlate features. CRFs are prone to overfitting, meaning that they do awesome for training data and horrible for new data. You could simulate this failnomenon by using the same training files over and over and over when building your feature set.
The authors are interested in discriminating between the containers and connectors in organization charts (see the figure at the top). They had 17 participants draw the chart shown, and ran 5 different algorithms to test the classification. The BCRFs proved to have the best recognition rates.
Discussion:
This paper was out of my league. Do you ever read something that makes you feel like you don't actually know anything about a given field? That was this paper. A little bit of side Googling returned some helpful links on BCRFs, etc., but I still felt lost. The results section basically showed me that everything they did was awesome and that it worked.
Yup, there is much mathematical detail in this paper. anyway, this is really nice paper to handle with incorporating context information into recogniton, which i am looking for.
ReplyDelete