We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
Applications like multimedia retrieval require efficient support for similarity search on large data collections. Yet, nearest neighbor search is a difficult problem in high dimen...
Arjen P. de Vries, Nikos Mamoulis, Niels Nes, Mart...
The purpose of our on going research would be to track entities, which enter their field of vision over the sensor network. Based on their sightings, they maintain a dynamic cache ...
Arvind Nath Rapaka, Sandeep Bogollu, Donald C. Wun...
This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, ...
Vassilis Athitsos, Jonathan Alon, Stan Sclaroff, G...
Sets of local features that are invariant to common image transformations are an effective representation to use when comparing images; current methods typically judge feature set...