The Power of Automatic Feature Selection: Rubine on Steroids

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dc.contributor.authorBlagojevic, Rachelen_US
dc.contributor.authorChang, Samuel Hsiao-Hengen_US
dc.contributor.authorPlimmer, Berylen_US
dc.contributor.editorMarc Alexa and Ellen Yi-Luen Doen_US
dc.date.accessioned2014-01-28T18:11:45Z
dc.date.available2014-01-28T18:11:45Z
dc.date.issued2010en_US
dc.description.abstractDigital ink features drive recognition engines. Intuitively, we understand that particular features are of more value for some problems than others. Likewise, inclusion of poor features may be detrimental to recognition success. Many different ink features have been proposed for ink recognition, and most work well for the context that they are employed. However given a new problem it is not clear which of the already defined features will be most useful. We have assembled and categorized a comprehensive feature library and use this with attribute selection algorithms to choose the best features for a specified problem. To verify the effectiveness of this approach the selected features are used to train a Rubine's recognizer. We show that a set of complementary features is most effective: poor features adversely affect recognition as do two or more aliases of good features. We have composed a variant of a Rubine recognizer for 3 different datasets and compared these with the Rubine's original features, a variant on this InkRubine and $1. The results show that feature selection can significantly improve recognition rates with this simple algorithm thus verifying our hypothesis that the right combination of features for a problem is one key to recognition success.en_US
dc.description.seriesinformationEurographics Workshop on Sketch-Based Interfaces and Modelingen_US
dc.identifier.isbn978-3-905674-25-5en_US
dc.identifier.issn1812-3503en_US
dc.identifier.urihttps://doi.org/10.2312/SBM/SBM10/079-086en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.7.5 [Document Capture]: Graphics recognition and interpretation, I.4.7 [Image Processing and Computer Vision]: Feature Measurement - feature representationen_US
dc.titleThe Power of Automatic Feature Selection: Rubine on Steroidsen_US
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SBM10: Sketch Based Interfaces and Modeling 2010