Recent Advances of Compact Hashing for Large-Scale Visual Search

Professor Shih-Fu Chang, Columbia University

Shih-Fu Chang

(Columbia University)

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Date: 12/05/2012


Finding nearest neighbor data in high-dimensional spaces is a common yet challenging task in many applications, such as stereo vision, image retrieval, and large graph construction. [ … ] Recent advances in locality sensitive hashing show promises by hashing high-dimensional features into a small number of bits while preserving proximity in the original feature space. [ … ] In this talk, I will first survey a few recent methods that extends basic hashing methods to incorporate labeled information through supervised and semi-supervised hashing, employ hyperplane hashing for finding nearest points to subspaces (e.g., planes), and demonstrate the practical utility of compact hashing methods in solving several challenging problems of large-scale mobile visual search – low bandwidth, limited processing power on mobile devices, and needs of searching large databases on servers. Finally, we study the fundamental questions of high-dimensional search – how is nearest neighbor search performance affected by data size, dimension, and sparsity; can we predict the performance of hashing methods over a data set before its implementation? (joint work with Junfeng He, Sanjiv Kumar, Wei Liu, and Jun Wang)

Created: Tuesday, December 4th, 2012