Similarity Search for Large-Scale Feature-Rich Data

16.01.2008

Foredragsholdere
Qin Lv

Date: Wednesday, January 16th 2008, at 10:15
Place: Lille Aud, Realfagsbygget, UiT
Title: " Similarity Search for Large-Scale Feature-Rich Data "
Lecturer: Assistant Professor Qin Lv, Computer Science Department, Stony Brook University (SUNY), USA.

Abstract:
Content-based similarity search for feature-rich data (such as digital photos, audio, video, and scientific sensor data) is a difficult problem due to the high dimensionality and usually massive amounts of data.

The main challenge is to achieve high-quality similarity search with high speed and low space usage.

This talk presents several techniques to address the problem of building efficient similarity search systems for large-scale feature-rich data. The first is a sketch construction algorithm for compact metadata representation, which can typically reduce the metadata size by an order of magnitude with minimal impact on search quality. The second is a multi-probe locality sensitive hashing (LSH) technique for indexing high-dimensional data, which substantially improves upon previous methods in both space and time efficiency. We have also developed Ferret, a general-purpose toolkit for building efficient similarity search systems. The Ferret toolkit has been successfully used to build similarity search systems for digital images, speech recordings, video, 3D shape models, and microarray gene expression data.

Bio:
Qin Lv is an Assistant Professor in the Computer Science Department, Stony Brook University (SUNY). She received her B.E. degree from Tsinghua University in 2000 and Ph.D. degree in Computer Science from Princeton University in 2006. Qin Lv's primary research interest is to develop efficient systems for managing and exploring massive amounts of digital data. Her research focuses on search systems, data management, distributed systems, storage systems and networking. Rooted in systems, her research also interacts with the areas of algorithm design, data mining, machine learning, and specific application domains such as multimedia, bio- informatics, sensor networks, healthcare, and scientific computing.