dc.contributor.author | Bozas, Konstantinos | |
dc.date.accessioned | 2015-07-20T12:01:14Z | |
dc.date.available | 2015-07-20T12:01:14Z | |
dc.date.issued | 26/11/2014 | |
dc.identifier.citation | Bozas, K. 2014. Scalable Image Retrieval based on Hand Drawn Sketches and their Semantic Information. Queen Mary University of London | en_US |
dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/7928 | |
dc.description.abstract | The 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.iso | en | en_US |
dc.publisher | Queen Mary University of London | |
dc.subject | Dentistry | en_US |
dc.subject | Tooth development | en_US |
dc.subject | Paediatric dentistry | en_US |
dc.title | Scalable Image Retrieval based on Hand Drawn Sketches and their Semantic Information | en_US |
dc.type | Thesis | en_US |
dc.rights.holder | The 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 | |