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A Distributed M-Tree for Similarity Search in Large Multimedia Database on Spark

A Distributed M-Tree for Similarity Search in Large Multimedia Database on Spark
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Author(s): Phuc Do (University of Information Techonology (VNU-HCM), Vietnam) and Trung Hong Phan (University of Information Techonology (VNU-HCM), Vietnam)
Copyright: 2020
Pages: 19
Source title: Handbook of Research on Multimedia Cyber Security
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India) and Deepak Gupta (LoginRadius Inc., Canada)
DOI: 10.4018/978-1-7998-2701-6.ch007

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Abstract

In this chapter, Image2vec or Video2vector are used to convert images and video clips to vectors in large multimedia database. The M-tree is an index structure that can be used for the efficient resolution of similarity queries on complex objects. M-tree can be profitably used for content-based retrieval on multimedia databases provided relevant features have been extracted from the objects. In a large multimedia database, to search for similarities such as k-NN queries and Range queries, distances from the query object to all remaining objects (images or video clips) are calculated. The calculation between query and entities in a large multimedia database is not feasible. This chapter proposes a solution to distribute the M-Tree structure on the Apache Spark framework to solve the Range Query and kNN Query problems in large multimedia database with a lot of images and video clips.

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