Cross-Modal Retrieval for CPSS Data

Data generated and collected from Cyber-Physical-Social Systems (CPSS) that usually in the forms of image, audio, video, and text, are complex and heterogeneous. How to deal with the cross-modal retrieval problem for heterogeneous CPSS data has drawn considerable interests recently. The hashing base...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Fangming Zhong [verfasserIn] Guangze Wang [verfasserIn] Zhikui Chen [verfasserIn] Feng Xia [verfasserIn] Geyong Min [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Cross-modal hashing nonlinear CPSS data Cloud-Fog-Edge computing

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 16689-16701
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:16689-16701

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.2967594

Katalog-ID:	DOAJ049752839

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callnumber-first	T - Technology
author	Fangming Zhong
spellingShingle	Fangming Zhong misc TK1-9971 misc Cross-modal hashing misc nonlinear misc CPSS data misc Cloud-Fog-Edge computing misc Electrical engineering. Electronics. Nuclear engineering Cross-Modal Retrieval for CPSS Data
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topic_title	TK1-9971 Cross-Modal Retrieval for CPSS Data Cross-modal hashing nonlinear CPSS data Cloud-Fog-Edge computing
topic	misc TK1-9971 misc Cross-modal hashing misc nonlinear misc CPSS data misc Cloud-Fog-Edge computing misc Electrical engineering. Electronics. Nuclear engineering
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title	Cross-Modal Retrieval for CPSS Data
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title_sort	cross-modal retrieval for cpss data
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title_auth	Cross-Modal Retrieval for CPSS Data
abstract	Data generated and collected from Cyber-Physical-Social Systems (CPSS) that usually in the forms of image, audio, video, and text, are complex and heterogeneous. How to deal with the cross-modal retrieval problem for heterogeneous CPSS data has drawn considerable interests recently. The hashing based methods have been widely studied in building bilateral semantic associations of binary codes for cross-model retrieval. However, most existing methods discard binary constraints and learn linear projections as hashing functions. Moreover, none of them consider the cross-modal retrieval application in the scenario of Cloud-Fog-Edge computing. Therefore, how to learn more compact and discriminative binary codes with discrete constraints and nonlinear hashing functions for CPSS data in the Cloud-Fog-Edge architecture is still an open problem. In this paper, we propose a nonlinear discrete cross-modal hashing (NDCMH) method based on concise binary classification for CPSS data which fully investigates the nonlinear relationship embedding, discrete optimization as well as the hashing functions learning. Different from previous methods, our work presents a concise but promising cross-modal hashing method that builds a direct connection between original CPSS data and binary codes, which can alleviate the impact of large quantization loss. Furthermore, we execute the cross-modal retrieval service at cloud and fog. Specifically, hashing functions are deployed at the fog plane to reduce the amount data transfer and storage need on the cloud. Extensive experiments carried out on typical CPSS datasets demonstrate that the proposed NDCMH significantly outperforms other state-of-the-art methods.
abstractGer	Data generated and collected from Cyber-Physical-Social Systems (CPSS) that usually in the forms of image, audio, video, and text, are complex and heterogeneous. How to deal with the cross-modal retrieval problem for heterogeneous CPSS data has drawn considerable interests recently. The hashing based methods have been widely studied in building bilateral semantic associations of binary codes for cross-model retrieval. However, most existing methods discard binary constraints and learn linear projections as hashing functions. Moreover, none of them consider the cross-modal retrieval application in the scenario of Cloud-Fog-Edge computing. Therefore, how to learn more compact and discriminative binary codes with discrete constraints and nonlinear hashing functions for CPSS data in the Cloud-Fog-Edge architecture is still an open problem. In this paper, we propose a nonlinear discrete cross-modal hashing (NDCMH) method based on concise binary classification for CPSS data which fully investigates the nonlinear relationship embedding, discrete optimization as well as the hashing functions learning. Different from previous methods, our work presents a concise but promising cross-modal hashing method that builds a direct connection between original CPSS data and binary codes, which can alleviate the impact of large quantization loss. Furthermore, we execute the cross-modal retrieval service at cloud and fog. Specifically, hashing functions are deployed at the fog plane to reduce the amount data transfer and storage need on the cloud. Extensive experiments carried out on typical CPSS datasets demonstrate that the proposed NDCMH significantly outperforms other state-of-the-art methods.
abstract_unstemmed	Data generated and collected from Cyber-Physical-Social Systems (CPSS) that usually in the forms of image, audio, video, and text, are complex and heterogeneous. How to deal with the cross-modal retrieval problem for heterogeneous CPSS data has drawn considerable interests recently. The hashing based methods have been widely studied in building bilateral semantic associations of binary codes for cross-model retrieval. However, most existing methods discard binary constraints and learn linear projections as hashing functions. Moreover, none of them consider the cross-modal retrieval application in the scenario of Cloud-Fog-Edge computing. Therefore, how to learn more compact and discriminative binary codes with discrete constraints and nonlinear hashing functions for CPSS data in the Cloud-Fog-Edge architecture is still an open problem. In this paper, we propose a nonlinear discrete cross-modal hashing (NDCMH) method based on concise binary classification for CPSS data which fully investigates the nonlinear relationship embedding, discrete optimization as well as the hashing functions learning. Different from previous methods, our work presents a concise but promising cross-modal hashing method that builds a direct connection between original CPSS data and binary codes, which can alleviate the impact of large quantization loss. Furthermore, we execute the cross-modal retrieval service at cloud and fog. Specifically, hashing functions are deployed at the fog plane to reduce the amount data transfer and storage need on the cloud. Extensive experiments carried out on typical CPSS datasets demonstrate that the proposed NDCMH significantly outperforms other state-of-the-art methods.
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title_short	Cross-Modal Retrieval for CPSS Data
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