Searching is an important tool for managing and navigating the massive amounts of data available in today'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 don'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 efficient and compact sketch descriptors. Second, the query retrieval subsystem considers sketches based on shape and structure similarity. Finally, a trained support vector machine classifier provides semantic filtering, which is then combined with median filtering to return the ranked results. SketchSeeker was tested on a large set of sketches against existing sketch similarity metrics, and it shows significant 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 find near similar sketches in real time.
