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How Well Do You Know Tom Hanks? Using a Game to Learn About Face Recognition

Oge Marques, Justyn Snyder and Mathias Lux Human face recognition abilities vastly outperform computer-vision algorithms working on comparable tasks, especially in the case of poor lighting, bad image quality, or partially hidden faces. In this paper,… → Read More: How Well Do You Know Tom Hanks? Using a Game to Learn About Face Recognition

HCIR site gets publication page

Over the past six years of the HCIR series of meetings, we’ve accumulated a number of publications. We’ve had a series of reports about the meetings, papers published in the ACM Digital Library, and an up-coming Special Issue of IP&M. In the run-up to this year’s event (stay tuned!), I decided it might be useful [...] → Read More: HCIR site gets publication page

Mobile Visual Search (MVS)

Once more, one year later An amazing presentation by  (my friend) Oge Marques!!!! Watch the (full length !!! ) video here: http://www.foerderverein-technische-fakultaet.at/2012/01/ruckblick-mobile-visual-search-video-slides/ Abstract Mobile Visua… → Read More: Mobile Visual Search (MVS)

Learning to Rank Research using Terrier

Part 1: http://terrierteam.blogspot.co.uk/2013/03/learning-to-rank-research-using-terrier.html Part 2: http://terrierteam.blogspot.gr/2013/03/learning-to-rank-research-using-terrier_26.html In recent years, the information retrieval (IR) field has expe… → Read More: Learning to Rank Research using Terrier

Learning to Rank Research Using Terrier – The Role of Features (Part 2)

This is the second post in a two-part series addressing our recent research in learning to rank. While the previous blog post addressed the role of the sample within learning to rank, and its impact on effectiveness and efficiency, in this blog post, I… → Read More: Learning to Rank Research Using Terrier – The Role of Features (Part 2)

Learning to Rank Research Using Terrier – The Role of Features (Part 2)

This is the second post in a two-part series addressing our recent research in learning to rank. While the previous blog post addressed the role of the sample within learning to rank, and its impact on effectiveness and efficiency, in this blog post, I… → Read More: Learning to Rank Research Using Terrier – The Role of Features (Part 2)

Learning to Rank Research Using Terrier – The Role of Features (Part 2)

This is the second post in a two-part series addressing our recent research in learning to rank. While the previous blog post addressed the role of the sample within learning to rank, and its impact on effectiveness and efficiency, in this blog post, I… → Read More: Learning to Rank Research Using Terrier – The Role of Features (Part 2)

QUALINET Multimedia Databases

Article from http://multimediacommunication.blogspot.gr/ A key for current and future developments in Quality of Experience resides in a rich and internationally recognized database of content of different sorts, and to share such a database with the s… → Read More: QUALINET Multimedia Databases

Explicit web search result diversification

A couple of weeks ago I successfully defended my PhD thesis at the School of Computing Science of the University of Glasgow. The thesis, entitled “Explicit web search result diversification”, was unconditionally approved with no corrections b… → Read More: Explicit web search result diversification

“There’s no bad data, only bad uses of data”

Article from http://www.nytimes.com/2013/03/24/technology/big-data-and-a-renewed-debate-over-privacy.html?smid=tw-share&_r=0 IN the 1960s, mainframe computers posed a significant technological challenge to common notions of privacy. That’s when t… → Read More: “There’s no bad data, only bad uses of data”