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2023 | OriginalPaper | Chapter

Binary Hyperdimensional Computing for Image Encoding

Authors : Jinghan Li, Jin Chen, Jiahui Liang, Sen Li, Baozhu Han, Hanlin Wu

Published in: Artificial Intelligence in China

Publisher: Springer Nature Singapore

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Abstract

Hyperdimensional computing uses hypervectors as basic patterns to construct cognitive codes to represent atomic entities through encodes different types of data into the same data structure based on hyperspace. In this paper, we exploit the reversibility of binary hyperdimensional computing to encode images to hypervectors and decode them back. We introduce turnover rate to properly separate the distance between adjacent values while maintaining the distance between them, so as to avoid the poor effect of segmentation or the direct generation that leads to the distance between adjacent hypervectors being too close to distinguish. We compared the performance of reversibility with the original hyperdimensional computing. The proposed approach has better performance.

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Metadata
Title
Binary Hyperdimensional Computing for Image Encoding
Authors
Jinghan Li
Jin Chen
Jiahui Liang
Sen Li
Baozhu Han
Hanlin Wu
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Artificial Intelligence in China

Print ISBN: 978-981-9912-55-1

Electronic ISBN: 978-981-9912-56-8

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-99-1256-8_6

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