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dc.contributor.authorBozas, Konstantinos
dc.date.accessioned2015-07-20T12:01:14Z
dc.date.available2015-07-20T12:01:14Z
dc.date.issued26/11/2014
dc.identifier.citationBozas, K. 2014. Scalable Image Retrieval based on Hand Drawn Sketches and their Semantic Information. Queen Mary University of Londonen_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/7928
dc.description.abstractThe research presented in this thesis aims to extend the capabilities of traditional content-based image retrieval systems, towards more expressive and scalable interactions. The study focuses on machine sketch understanding and its applications. In particular, sketch based image retrieval (SBIR), a form of image search where the query is a user drawn picture (sketch), and freehand sketch recognition. SBIR provides a platform for the user to express image search queries that otherwise would be di cult to describe with text. The research builds upon two main axes: extension of the state-of-the art and scalability. Three novel approaches for sketch recognition and retrieval are presented. Notably, a patch hashing algorithm for scalable SBIR is introduced, along with a manifold learning technique for sketch recognition and a horizontal ip-invariant sketch matching method to further enhance recognition accuracy. The patch hashing algorithm extracts several overlapping patches of an image. Similarities between a hand drawn sketch and the images in a database are ranked through a voting process where patches with similar shape and structure con guration arbitrate for the result. Patch similarity is e ciently estimated with a hashing algorithm. A spatially aware index structure built on the hashing keys ensures the scalability of the scheme and allows for real time re-ranking upon query updates. Sketch recognition is achieved through a discriminant manifold learning method named Discriminant Pairwise Local Embeddings (DPLE). DPLE is a supervised dimensionality reduction technique that generates structure preserving discriminant subspaces. This objective is achieved through a convex optimization formulation where Euclidean distances between data pairs that belong to the same class are minimized, while those of pairs belonging to di erent classes are maximized. A scalable one-to-one sketch matching technique invariant to horizontal mirror re ections further improves recognition accuracy without high computational cost. The matching is based on structured feature correspondences and produces a dissimilarity score between two sketches. Extensive experimental evaluation of our methods demonstrates the improvements over the state-of-the-art in SBIR and sketch recognition.en_US
dc.language.isoenen_US
dc.publisherQueen Mary University of London
dc.subjectDentistryen_US
dc.subjectTooth developmenten_US
dc.subjectPaediatric dentistryen_US
dc.titleScalable Image Retrieval based on Hand Drawn Sketches and their Semantic Informationen_US
dc.typeThesisen_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author


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    Theses Awarded by Queen Mary University of London

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