KimCHI is a specialized sketch recognition technique to ananlyse children's sketches to asses their developmental progress. It distinguishes between developmental stage within age group and also gender among children.
To find the optimal subset features we use BestFirst selection built into weka system with 10 fold croos validation.
Optimal features for classifying preschoolers vs grade schoolers
Average curvature of the stroke (100%)
+ Direction change ratio (100%)
+ The error of the best fit line of the direction graph (100%)
+ The maximum curvature to average curvature value (100%)
Optimal features for classifying preschoolers vs adults
Average curvature of the stroke (100%)
+ Direction change ratio (100%)
+ The angle of the major axis relative to center (100%)
+ The error of the best fit line of the direction graph (100%)
+ The maximum curvature to average curvature value (100%)
+ Slope of the direction graph (100%)
Classifier Set:
Bayes Net, BFTree, ADTree, MultilayerPerceptron, Naive Bayes, Random Tree, Random Forest, and RBFNetwork
Best classifier for recognizing age: Random forest classifier and Bagging.
Optimal features for classifying genders:
Stroke density (100%) +
A density metric for the gesture stroke that uses the stroke’s length
and bounding box size (100%) +
Direction change ratio (70%)
Best Classifier : Bayes Net
My thoughts: This gives us insight of how we can use sketches in psychology. Such recognition techniques should be used in finding disabilities as well. Features like pen pressure, tilt also can be considered to find the developmental stage of children.
To find the optimal subset features we use BestFirst selection built into weka system with 10 fold croos validation.
Optimal features for classifying preschoolers vs grade schoolers
Average curvature of the stroke (100%)
+ Direction change ratio (100%)
+ The error of the best fit line of the direction graph (100%)
+ The maximum curvature to average curvature value (100%)
Optimal features for classifying preschoolers vs adults
Average curvature of the stroke (100%)
+ Direction change ratio (100%)
+ The angle of the major axis relative to center (100%)
+ The error of the best fit line of the direction graph (100%)
+ The maximum curvature to average curvature value (100%)
+ Slope of the direction graph (100%)
Classifier Set:
Bayes Net, BFTree, ADTree, MultilayerPerceptron, Naive Bayes, Random Tree, Random Forest, and RBFNetwork
Best classifier for recognizing age: Random forest classifier and Bagging.
Optimal features for classifying genders:
Stroke density (100%) +
A density metric for the gesture stroke that uses the stroke’s length
and bounding box size (100%) +
Direction change ratio (70%)
Best Classifier : Bayes Net
My thoughts: This gives us insight of how we can use sketches in psychology. Such recognition techniques should be used in finding disabilities as well. Features like pen pressure, tilt also can be considered to find the developmental stage of children.
I agree with the additional pen positioning factors that can be used as features. I think analyzing in some way how pre-school and grade school children hold their pen can be a significant feature as well, since pen holding is often different between children of different ages and is one of the key factors in teaching children how to write and draw.
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