Robust Classification of Strokes with SVM and Grouping

Nataneli, Gabriele; Faloutsos, Petros

doi:10.1007/978-3-540-76858-6_8

Cited by 2 publications

(2 citation statements)

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“…Both techniques rely on fixed heuristics or parser rules and capture only a specific sketching domain. Nataneli & Faloutsos [12] and Zhou et al [22] present SVM-based workflows for stroke classification and grouping and letter segmentation in handwritten Japanese text, respectively. Except for the spatial structural approach of Nataneli & Faloutsos [12], all of the aforementioned works employ the creation history of a sketched scene for data organization and analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Nataneli & Faloutsos [12] and Zhou et al [22] present SVM-based workflows for stroke classification and grouping and letter segmentation in handwritten Japanese text, respectively. Except for the spatial structural approach of Nataneli & Faloutsos [12], all of the aforementioned works employ the creation history of a sketched scene for data organization and analysis. Sezgin & Davis provide empirical evidence for per-user predictability of stroke orderings for recurring known objects [15].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Sketch-Based Interfaces: Exploiting Spatio-temporal Context for Automatic Stroke Grouping

Dickmann¹,

Lensing²,

Porzel³

et al. 2010

Smart Graphics

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Abstract. In this paper, we investigate how discourse context in the form of short-term memory can be exploited to automatically group consecutive strokes in digital freehand sketching. With this machine learning approach, no database of explicit object representations is used for template matching on a complete scene-instead, grouping decisions are based on limited spatio-temporal context. We employ two different classifier formalisms for this time series analysis task, namely Echo State Networks (ESNs) and Support Vector Machines (SVMs). ESNs present internal-state classifiers with inherent memory capabilities. For the conventional static SVM, short-term memory is supplied externally via fixed-length feature vector expansion. We compare the respective setup heuristics and conduct experiments with two exemplary problems. Promising results are achieved with both formalisms. Yet, our experiments indicate that using ESNs for variable-length memory tasks alleviates the risk of overfitting due to non-expressive features or improperly determined temporal embedding dimensions.

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Section: Related Workmentioning

confidence: 99%