As part of my visit to the I3S Lab, I’m giving a talk on February 11th :
Title: Scalability Issues in Multimedia Information Retrieval
Where: I3S conference room (level 0)
When: Monday, February 11th, at 14h00
The Millennium marked a turning point for textual Information Retrieval, a moment when Search Engines and Social Networks changed our relationship to World Wide Web: gigantic corpora of knowledge suddenly felt friendly, accessible and manageable. Ten years later, the same phenomenon is happening for complex non-textual data, including multimedia. The challenge is how to provide intuitive, convenient, fast services for those data, in collections whose size and growing rate is so big, that our intuitions fail to grasp.
Two issues have dominated the scientific discourse when we aim at that goal: our ability to represent multimedia information in a way that allows answering the high-level queries posed by the users, and our ability to process those queries fast.
In this talk, I will focus on the latter issue, examining similarity search in high-dimensional spaces, a pivotal operation found a variety of database applications — including Multimedia Information Retrieval. Similarity search is conceptually very simple: find the objects in the dataset that are similar to the query, i.e., those that are close to the query according to some notion of distance. However, due to the infamous “curse of the dimensionality”, performing it fast is challenging from both the theoretical and the practical point-of-view.
I have selected for this talk Hypercurves, my latest research endeavor, which is a distributed technique aimed at hybrid CPU–GPU environments. Hypercurves’ goal is to employ throughput-oriented GPUs to keep answer times optimal, under several load regimens. The parallelization also poses interesting theoretical questions of how much can we optimize the parallelization of approximate k-nearest neighbors, if we relax the equivalence to the sequential algorithm from exact to probabilistic.
The talk will be in English. I thank my colleague and friend Prof. Frédéric Precioso, for this opportunity.