Bibliography:
Tevfik Metin Sezgin and Randall Davis. HMM-Based Efficient Sketch Recognition. Proceedings of the 10th international conference on Intelligent user interfaces. pp. 281-283. 2005.
Summary:
This paper talks about using Hidden Markov Models to recognize sketches. This technique can be used to identify sketches, when sketch data is collected incrementally with (x, y, time) coordinates. This is different from traditional methods where sketches were treated as images.
The method presented in the paper identifies the sketching style of individual users. Thus, rather than a generalized recognition system, the recognizer works well for a user with a specific style. From their user studies, it was found that individual sketching styles persist across sketches. This structure was captured using Hidden markov models. The hmms were trianed on input sketch data, partitioned by length, and the formulation was done using graphs.
The system was evaluated in 2 parts: 1) Evaluating the HMMs with real data. 2) Compare the performance of the algorithm to a baseline method. The HMMs were found to have a high accuracy, and performance improved with more training data. The base line model used to compare HMMs was a feature based pattern-matching system, without ordering information. The HMM based system was found to scale well (by time) when number of objects in the scene increased.
Discussion:
Its interesting to see that sketching style of users persists across sketches. This makes it possible to train sketches on multiple feature based techniques for each user seperately. One issue I see this method, is that it's hard for new information learned to be relayed to the system. (I think Dr. Hammond mentioned this in class)
Tevfik Metin Sezgin and Randall Davis. HMM-Based Efficient Sketch Recognition. Proceedings of the 10th international conference on Intelligent user interfaces. pp. 281-283. 2005.
Summary:
This paper talks about using Hidden Markov Models to recognize sketches. This technique can be used to identify sketches, when sketch data is collected incrementally with (x, y, time) coordinates. This is different from traditional methods where sketches were treated as images.
The method presented in the paper identifies the sketching style of individual users. Thus, rather than a generalized recognition system, the recognizer works well for a user with a specific style. From their user studies, it was found that individual sketching styles persist across sketches. This structure was captured using Hidden markov models. The hmms were trianed on input sketch data, partitioned by length, and the formulation was done using graphs.
The system was evaluated in 2 parts: 1) Evaluating the HMMs with real data. 2) Compare the performance of the algorithm to a baseline method. The HMMs were found to have a high accuracy, and performance improved with more training data. The base line model used to compare HMMs was a feature based pattern-matching system, without ordering information. The HMM based system was found to scale well (by time) when number of objects in the scene increased.
Discussion:
Its interesting to see that sketching style of users persists across sketches. This makes it possible to train sketches on multiple feature based techniques for each user seperately. One issue I see this method, is that it's hard for new information learned to be relayed to the system. (I think Dr. Hammond mentioned this in class)
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