Approximate nearest neighbors using sparse representations

A new method is introduced that makes use of sparse image representations to search for approximate nearest neighbors (ANN) under the normalized inner-product distance. The approach relies on the construction of a new sparse vector designed to approximate the normalized inner-product between underlying signal vectors. The resulting ANN search algorithm shows significant improvement compared to querying with the original sparse vectors. The system makes use of a proposed transform that succeeds in uniformly distributing the input dataset on the unit sphere while preserving relative angular distances.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Joaquin Zepeda, Ewa Kijak, Christine Guillemot
• Keywords: ICASSP 2010, Normalized Inner-product, Signal Processing, Sparse Image Representations, Sparse Vectors