I’ve presented the paper “Fast Identification of Visual Documents Using Local Descriptors” on the 8th ACM Symposium on Document Engineering — DocEng 2008. This paper had a stronger emphasis in the application (image identification, also known as copy detection or — my least favourite nomenclature — near-duplicate detection) than in the technicalities of multimedia indexing and descriptor matching (which were, in the end of the day, the staple of my Ph.D. work). But it was the opportunity to tell the world about the projection KD-forest, the second (among three) original technique for high-dimensional indexing.
Anyway, here’s the abstract: “In this paper we introduce a system for the identification of visual documents. Since it stems from content-based document indexing and retrieval, our system does not need to rely on textual annotations, watermarks or other metadata, which can be missing or incorrect. Our retrieval system is based on local descriptors, which have been shown to provide accurate and robust description. Because of the high computational costs associated to the matching of local descriptors, we propose Projection KD-Forest: an indexing technique which allows efficient approximate k nearest neighbors search. Experiments demonstrate that the Projection KD-Forest allows the system to provide prompt results with negligible loss on accuracy. The Projection KD-Forest also compares well when contrasted to other strategies of k nearest neighbors search.”
I guess that the dream of all conference organisers is to amass hundreds of participants and to have an acceptance rate as close to zero as possible. This edition of DocEng was nothing like that — I think that we were 60 or 70 counting everyone, authors and participants. Yet, in terms of exchange of scientific ideas, it was one of the best conferences I’ve ever participated.
The paper, and related material, can be found in my publications page.