Thursday, September 11, 2008

Prototype Pruning by Feature Extraction

Watt, Xie

COMMENTS

1. Comment on Daniel's blog

SUMMARY

In the paper the author discusses about gesture recognition where the set of symbols is very large, defines a set of features and analyses the performance of the recogniser after implementing them.The author introduces the concept of pruning, in which a symbol is first classified into a group, and the reclassified into a particular symbol in that group. The author also discusses stroke pre-processing techniques like chopping the head and tail, resampling, smoothing. The author suggests certain new geometric features like Number of loops, Minimum distance pair, Number of cusps and number of intersections. The author also suggests using some ink related features like number of strokes, point density; directional features like initial and end directions and global features like initial and end points. The results show that with the use of features the accuracy was reduced to 91 percent, but the prototypes were pruned thus reducing the computation proportionately.

DISCUSSION

One of the most interesting idea about this paper was the concept of pruning. I think without pruning it is not possible to recognize among a large set accurately. I had thought of the same idea to implement in my recognizer, before reading this. I was trying to form a set of similar alphabets (like Os and Ds) which were misclassified frequently and the train a different weight vector for classifying among these similar symbols. However, I did not get encouraging results probable due to erroneous test data. I would like to explore the concept of pruning further.

1 comment:

Daniel said...

Well, gesture recognition is possible on a large set without pruning, it's just more computationally expensive.

The dangerous part is truly identifying the unique features of a prototype or group so as to eliminate false negatives. This is where context might come into play when dealing with a large corpus and possibly identifying a domain. For example, if everything I sketched before this was a letter, then what I'm likely to sketch next is also probably a letter as opposed to a shape. This obviously has its own faults, too.