Locality-sensitive hashing

About: Locality-sensitive hashing is a research topic. Over the lifetime, 1894 publications have been published within this topic receiving 69362 citations.

A survey on image retrieval performance of different bag of visual words indexing techniques

Abstract: In this paper a survey has been carried out over image retrieval performances of bag of visual words (BoVW) method using different indexing techniques. Bag of visual word method is a content based image retrieval technique, where images are represented as a sparse vector of occurrences of visual words. In this paper different indexing techniques are used to compute near similar visual word vectors of a query image. Locality sensitive hashing, SR-tree based indexing and naive L1 and L2 norm based distance metric calculation are used here. Standard datasets like, UKBench [19], holiday dataset [9] and images from SMARAK1 are used for performance analysis.

Scalable clip-based near-duplicate video detection with ordinal measure

Abstract: Detection of duplicate or near-duplicate videos on large-scale database plays an important role in video search. In this paper, we analyze the problem of near-duplicates detection and propose a practical and effective solution for real-time large-scale video retrieval. Unlike many existing approaches which make use of video frames or key-frames, our solution is based on a more discriminative signature of video clips. The feature used in this paper is an extension of ordinal measures which have proven to be robust to change in brightness, compression formats and compression ratios. For efficient retrieval, we propose to use Multi-Probe Locality Sensitive Hashing (MPLSH) to index the video clips for fast similarity search and high recall. MPLSH is able to filter out a large number of dissimilar clips from video database. To refine the search process, we apply a slightly more expensive clip-based signature matching between a pair of videos. Experimental results on the data set of 12, 790 videos [26] show that the proposed approach achieves at least 6.5% average precision improvement over the baseline color histogram approach while satisfying real-time requirements. Furthermore, our approach is able to locate the frame offset of copy segment in near-duplicate videos.

R2LSH: A Nearest Neighbor Search Scheme Based on Two-dimensional Projected Spaces

Abstract: Locality sensitive hashing (LSH) is a widely practiced c-approximate nearest neighbor (c-ANN) search algorithm because of its appealing theoretical guarantee and empirical performance. However, available LSH-based solutions do not achieve a good balance between cost and quality because of certain limitations in their index structures.In this paper, we propose a novel and easy-to-implement disk- based method named R2LSH to answer ANN queries in highdimensional spaces. In the indexing phase, R2LSH maps data objects into multiple two-dimensional projected spaces. In each space, a group of B+-trees is constructed to characterize the corresponding data distribution. In the query phase, by setting a query-centric ball in each projected space and using a dynamic counting technique, R2LSH efficiently determines candidates and returns query results with the required quality. Rigorous theoretical analysis reveals that the proposed algorithm supports c-ANN search for arbitrarily small c ≥ 1 with probability guarantee. Extensive experiments on real datasets verify the superiority of R2LSH over state-of-the-art methods.

A Generic Inverted Index Framework for Similarity Search on the GPU

Abstract: We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types. Not every data type and similarity measure are supported by GENIE, but many popular ones are. We present the system design of GENIE, and demonstrate similarity search with GENIE on several data types along with a theoretical analysis of search results. A new concept of locality sensitive hashing (LSH) named tau-ANN search, and a novel data structure c-PQ on the GPU are also proposed for achieving this purpose. Extensive experiments on different real-life datasets demonstrate the efficiency and effectiveness of our framework. The implemented system has been released as open source: https://github.com/SeSaMe-NUS/genie.