mmLSH: A Practical and Efficient Technique for Processing Approximate Nearest Neighbor Queries on Multimedia Data

Abstract

Many large multimedia applications require efficient processing of nearest neighbor queries. Often, multimedia data are represented as a collection of important high-dimensional feature vectors. Locality Sensitive Hashing (LSH) is a very popular approximate technique for finding nearest neighbors in high-dimensional spaces. In order to find top-k similar multimedia objects, existing LSH techniques require users to find top-k similar feature vectors for each of the feature vectors that represent the query object. This leads to wasted and redundant work due to two main reasons: 1) not all feature vectors may contribute equally in finding the top-k similar multimedia objects, and 2) feature vectors are treated independently during query processing. Additionally, there is no theoretical guarantee on the returned multimedia results. In this work, we propose a practical and efficient indexing approach for finding top-k approximate nearest neighbors for multimedia data using LSH, called mmLSH. In mmLSH, we present novel strategies to find nearest neighbor objects for a given multimedia object query. We also provide theoretical guarantees on the returned multimedia results. Additionally, we present a buffer-conscious strategy to speedup the query processing. Experimental evaluation shows significant gains in performance time and accuracy for different real multimedia datasets when compared against state-ofthe-art LSH techniques.