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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Multi-kernel Hashing with Semantic Correlation Maximization for Cross-Modal Retrieval

verfasst von : Guangfei Yang, Huanghui Miao, Jun Tang, Dong Liang, Nian Wang

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

Cross-modal hashing aims to facilitate approximate nearest neighbor search by embedding multimedia data represented in high-dimensional space into a common low-dimensional Hamming space, which serves as a key part in multimedia retrieval. In recent years, kernel-based hashing methods have achieved impressive success in cross-modal hashing. Enlightened by this, we present a novel multiple kernel hashing method, where hash functions are learned in the kernelized space using a sequential optimization strategy. Experimental results on two benchmark datasets verify that the proposed method significantly outperforms some state-of-the-art methods.

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Vorheriges Kapitel A Hierarchical Voting Scheme for Robust Geometric Model Fitting
Nächstes Kapitel Preprocessing and Segmentation Algorithm for Multiple Overlapped Fiber Image
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Metadaten
Titel
Multi-kernel Hashing with Semantic Correlation Maximization for Cross-Modal Retrieval
verfasst von
Guangfei Yang
Huanghui Miao
Jun Tang
Dong Liang
Nian Wang
Copyright-Jahr
2017
Buch
Image and Graphics

Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-71607-7_3