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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

Phuc Do, Trung Hong Phan
Copyright: © 2020 |Pages: 19
ISBN13: 9781799827016|ISBN10: 1799827011|EISBN13: 9781799827023
DOI: 10.4018/978-1-7998-2701-6.ch007
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MLA

Do, Phuc, and Trung Hong Phan. "A Distributed M-Tree for Similarity Search in Large Multimedia Database on Spark." Handbook of Research on Multimedia Cyber Security, edited by Brij B. Gupta and Deepak Gupta, IGI Global, 2020, pp. 146-164. https://doi.org/10.4018/978-1-7998-2701-6.ch007

APA

Do, P. & Phan, T. H. (2020). A Distributed M-Tree for Similarity Search in Large Multimedia Database on Spark. In B. Gupta & D. Gupta (Eds.), Handbook of Research on Multimedia Cyber Security (pp. 146-164). IGI Global. https://doi.org/10.4018/978-1-7998-2701-6.ch007

Chicago

Do, Phuc, and Trung Hong Phan. "A Distributed M-Tree for Similarity Search in Large Multimedia Database on Spark." In Handbook of Research on Multimedia Cyber Security, edited by Brij B. Gupta and Deepak Gupta, 146-164. Hershey, PA: IGI Global, 2020. https://doi.org/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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