This paper discusses the hybrid approach of combing geometric-based and gesture-based recognition to produce a system with high recognition rate and providing normalized confidence values for alternative interpretations. It takes advantage of positive aspects of each.
Gesture-based Recognition: It focuses on how a sketch is drawn, not how the final sketch looks like. It takes an input stroke and classifies each one into pre-defined gestures.
Advantage: Fast classification along with normalized confidence values
Disadvantage: Features are user-dependent and require individual training.
Geometric-based Recognition: It focuses on how the sketch looks like and less on how it is actually drawn. It takes single stroke as input and classifies them as one of predefined geometric primitives using formulas based on geometry. Primitives are combined to give more complex shapes using grammars.
Advantage: More user and style independent
Disadvantage: Use thresholds and heuristic hierarchies which are difficult to analyze and optimize in a systematic fashion. Inferences about generalization are hard to determine
Inferences about generalization is hard to determine because classification isn' statistical
Ranking alternative measure becomes difficult because of use of multiple error measure.
Ranking alternative interpretations with normalized confidence value aids in higher level recognition system which has access to context to resolve ambiguity in lower level interpretation.
The author combines the features of both gesture-based Rubine and geometric-based PaleoSketch giving a total of 44 features(31 from PaleoSketch and 13 from Rubine) and uses quadratic classifier.
Experiment: Data was collected from 20 users, each user giving 90 samples. The quadratic classifier was used to classify single stroke sketched primitives using 44 features.
Feature subset selection: Optimal subset selection was done using Sequential forward selection. 10 folds of SFS was performed using rrandom50 user splits giving 10 subsets. To determine the final optimal feature 25 folds of classification using random 50/50 was performed on each of 10 subsets. 10 other subsets were created. The first of this has features that were present in all 10 folds of subset collection, the second had features that were present in at least 9 of 10 folds and so forth. The best performance was of the subset with features that were present in atleast 50% of subset selection process.
Using this subset we achieved 97.44% of accuracy, close to PaleoSketch(98.56%). The classification difference between this approach and PaleoSketch is insignificant.
The susbet consists of 14 features, 13 from PaleoSketch and only one from Rubine.
Advantages of this approach: Faster classification because it uses lesser features.
Gesture-based Recognition: It focuses on how a sketch is drawn, not how the final sketch looks like. It takes an input stroke and classifies each one into pre-defined gestures.
Advantage: Fast classification along with normalized confidence values
Disadvantage: Features are user-dependent and require individual training.
Geometric-based Recognition: It focuses on how the sketch looks like and less on how it is actually drawn. It takes single stroke as input and classifies them as one of predefined geometric primitives using formulas based on geometry. Primitives are combined to give more complex shapes using grammars.
Advantage: More user and style independent
Disadvantage: Use thresholds and heuristic hierarchies which are difficult to analyze and optimize in a systematic fashion. Inferences about generalization are hard to determine
Inferences about generalization is hard to determine because classification isn' statistical
Ranking alternative measure becomes difficult because of use of multiple error measure.
Ranking alternative interpretations with normalized confidence value aids in higher level recognition system which has access to context to resolve ambiguity in lower level interpretation.
The author combines the features of both gesture-based Rubine and geometric-based PaleoSketch giving a total of 44 features(31 from PaleoSketch and 13 from Rubine) and uses quadratic classifier.
Experiment: Data was collected from 20 users, each user giving 90 samples. The quadratic classifier was used to classify single stroke sketched primitives using 44 features.
Feature subset selection: Optimal subset selection was done using Sequential forward selection. 10 folds of SFS was performed using rrandom50 user splits giving 10 subsets. To determine the final optimal feature 25 folds of classification using random 50/50 was performed on each of 10 subsets. 10 other subsets were created. The first of this has features that were present in all 10 folds of subset collection, the second had features that were present in at least 9 of 10 folds and so forth. The best performance was of the subset with features that were present in atleast 50% of subset selection process.
Using this subset we achieved 97.44% of accuracy, close to PaleoSketch(98.56%). The classification difference between this approach and PaleoSketch is insignificant.
The susbet consists of 14 features, 13 from PaleoSketch and only one from Rubine.
Advantages of this approach: Faster classification because it uses lesser features.
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