I’ve passed the viva-voce defense of my Ph.D. thesis “Local-Descriptor Matching for Image Identification Systems”, thus completing the program.
I won’t pretend that it is not a relief to have survived the process, it certainly wasn’t easy!
But I have been lucky enough to work with a subject about which I am passionate, and with people who are amazing. So, even though I’ve got the much sought after title of “Dr.” I expect to keep on the same fruitful research track (subject, team) for a while.
The abstract of the thesis: “Image identification (or copy detection) consists in retrieving the original from which a query image possibly derives, as well as any related metadata, such as titles, authors, copyright information, etc. The task is challenging because of the variety of transformations that the original image may have suffered. Image identification systems based on local descriptors have shown excellent efficacy, but often suffer from efficiency issues, since hundreds, even thousands of descriptors, have to be matched in order to find a single image. The objective of our work is to provide fast methods for descriptor matching, by creating efficient ways to perform the k-nearest neighbours search in high-dimensional spaces. In this way, we can gain the advantages from the use of local descriptors, while minimising the efficiency issues. We propose three new methods for the k-nearest neighbours search: the 3-way trees — an improvement over the KD-trees using redundant, overlapping nodes; the projection KD-forests — a technique which uses multiple moderate dimensional KD-trees; and the multicurves, which is based on multiple moderate dimensional Hilbert space-filling curves. Those techniques try to reduce the amount of random access to the data, in order to be well adapted to the implementation in secondary memory.”
The full text, and other goodies, are available in my publications page.