High-Dimensional Indexing for Video Retrieval

Calistru, Catalin; Ribeiro, Cristina; David, Gabriel

doi:10.5772/37303

Cited by 2 publications

(1 citation statement)

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“…Thus, it provides slightly less accurate results, but offers in return a sharp reduction in computation time. We find in this family methods such VA-File [8] and its variants [24], [30], the LSH methods [15], [17] and its variants [9], [13], [14], [25], BitMatrix [10], KRA +-Block [11], multi-dimensional inverted index [22], etc. Unluckily, most of indexing and search structures available today have several limitations including scalability:1) a relatively large computation time 2) limiting memory 3) degradation of research quality.…”

Section: Introductionmentioning

confidence: 99%

Locality sensitive hashing for content based image retrieval: A comparative experimental study

Chafik

Daoudi

Ouardi

et al. 2014

2014 International Conference on Next Generation Networks and Services (NGNS)

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This paper presents a comparative experimental study of the multidimensional indexing methods based on the approximation approach. We are particularly interested in the LSH family, which provides efficient index structures and solves the dimensionality curse problem. The goal is to understand the performance gain and the behavior of this family of methods on large-scale databases. E2LSH is compared to the KRA+-Blocks and the sequential scan methods. Two criteria are used in evaluating the E2LSH performances, namely average precision and CPU time using a database of one million image descriptors. Keywords-Content based image retrieval (CBIR), Curse of dimensionality, Locality sensitive hashing, Multidimensional indexing, Scalability.I.

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