Approximate nearest neighbors using sparse representations

Joaquin Zepeda (1) , Ewa Kijak (2) , Christine Guillemot (1)

1 TEMICS - Digital image processing, modeling and communication
2 TEXMEX - Multimedia content-based indexing

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.

Domains: Signal and Image Processing; Signal and Image processing

Joaquin Zepeda, Ewa Kijak, Christine Guillemot. Approximate nearest neighbors using sparse representations. IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'10, IEEE, Mar 2010, Dallas, TX, United States. ⟨10.1109/ICASSP.2010.5496145⟩. ⟨inria-00561778⟩