November 10, 2010 @ 11:30am : Sanjiv Kumar (Google Research, NY)

Compact Hash Codes for Scalable Matching

Sanjiv Kumar (Google Research, NY)

Hashing based Approximate Nearest Neighbor (ANN) search in huge databases has attracted much attention recently due to their fast query time and drastically reduced storage needs. Linear projection based methods are particularly of great interest because of their simplicity and efficiency. Moreover, they have yielded state-of-the-art performance on many tasks. However, most of these methods either use random projections or extract principal directions from the data to learn hash functions. The resulting embedding suffers from poor discrimination when compact codes are used. In this talk I will describe a simple data-dependent projection learning method such that each hash function is designed to correct the errors made by the previous one sequentially. The proposed method easily adapts to both unsupervised and semi-supervised scenarios and shows significant performance gains over the state-of-the-art methods. I will also describe how one can speed up the retrieval further by orders of magnitude using novel structures called tree-hash hybrids.

seminars/seminaritems/2010-11-10.txt · Last modified: 2011/02/27 17:11 by yann