Permutation grouping: intelligent Hash function design for audio & image retrieval

The combination of MinHash-based signatures and locality- sensitive hashing (LSH) schemes has been effectively used for finding approximate matches in very large audio and image retrieval systems. In this study, we introduce the idea of permutation-grouping to intelligently design the hash functions that are used to index the LSH tables. This helps to overcome the inefficiencies introduced by hashing real-world data that is noisy, structured, and most importantly is not independently and identically distributed. Through extensive tests, we find that permutation-grouping dramatically increases the efficiency of the overall retrieval system by lowering the number of low-probability candidates that must be examined by 30-50%.

[1]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[2]  David Salesin,et al.  Fast multiresolution image querying , 1995, SIGGRAPH.

[3]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[4]  Edith Cohen,et al.  Finding interesting associations without support pruning , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[5]  Ton Kalker,et al.  A Highly Robust Audio Fingerprinting System , 2002, ISMIR.

[6]  Derek Hoiem,et al.  Computer vision for music identification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Michael A. Casey,et al.  Song Intersection by Approximate Nearest Neighbor Search , 2006, ISMIR.

[8]  Shumeet Baluja,et al.  Audio Fingerprinting: Combining Computer Vision & Data Stream Processing , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.