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    SketchSeeker : Finding Similar Sketches

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    Searching is an important tool for managing and navigating the massive amounts of data available in todayā€™s information age. While new searching methods have be-come increasingly popular and reliable in recent years, such as image-based searching, these methods are more limited than text-based means in that they donā€™t allow generic user input. Sketch-based searching is a method that allows users to draw generic search queries and return similar drawn images, giving more user control over their search content. In this thesis, we present Sketchseeker, a system for indexing and searching across a large number of sketches quickly based on their similarity. The system includes several stages. First, sketches are indexed according to eļ¬ƒcient and compact sketch descriptors. Second, the query retrieval subsystem considers sketches based on shape and structure similarity. Finally, a trained support vector machine classiļ¬er provides semantic ļ¬ltering, which is then combined with median ļ¬ltering to return the ranked results. SketchSeeker was tested on a large set of sketches against existing sketch similarity metrics, and it shows signiļ¬cant improvements in both speed and accuracy when compared to existing known techniques. The focus of this thesis is to outline the general components of a sketch retrieval system to ļ¬nd near similar sketches in real time
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