features - Features Extraction for SketchBased Recognition...

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1 Fall 2008 CAP 6938 – Topics in Pen-Based User Interfaces ©Joseph J. LaViola Jr. Features Extraction for Sketch- Based Recognition Lecture #8: Feature Extraction Joseph J. LaViola Jr. Fall 2008 Fall 2008 CAP 6938 – Topics in Pen-based User Interfaces ©Joseph J. LaViola Jr. Recall Pen-Based Interface Dataflow Raw Stroke Data Preprocessing Segmentation Feature Extraction And Analysis Classification Ink Parsing Sketch Understanding Make Inferences
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2 Fall 2008 CAP 6938 – Topics in Pen-based User Interfaces ©Joseph J. LaViola Jr. Feature Extraction and Analysis What came first, the feature or the machine learning algorithm? Want to distinguish sketch components from one another Good features are critical Extract important information geometrical, statistical, contextual Examples include arc length, histograms, cusps, aspect ratio self-intersections, stroke area, etc… Fall 2008 CAP 6938 – Topics in Pen-based User Interfaces ©Joseph J. LaViola Jr. Finding Features Challenging problem need fast algorithms for gathering information features must be good discriminators Often trial and error Can be domain specific
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3 Fall 2008 CAP 6938 – Topics in Pen-based User Interfaces ©Joseph J. LaViola Jr. Geometric Features (1) Number of strokes if you know how many strokes a symbol has, you can break up your recognizer into pieces (i.e., recognizer for 1 stroke symbols, recognizer for 2 stroke symbols …) Cusps smooth vs. jagged strokes distance between cusps useful for when cusps are close together/far apart Fall 2008 CAP 6938 – Topics in Pen-based User Interfaces ©Joseph J. LaViola Jr. Geometric Features (2) Aspect ratio (width / height) tall vs. flat Self Intersections loops vs. no loops strokes with write over distance between self intersections also useful use line segment intersection algorithm loops write over
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4 Fall 2008 CAP 6938 – Topics in Pen-based User Interfaces ©Joseph J. LaViola Jr.
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