Exploiting hierarchical activations of neural network for image retrieval

Li, Y; Kong, X; Zheng, L; Tian, Q

Exploiting hierarchical activations of neural network for image retrieval

Li, Y Kong, X Zheng, L

Tian, Q

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Publication Type:: Conference Proceeding
Citation:: MM 2016 - Proceedings of the 2016 ACM Multimedia Conference, 2016, pp. 132 - 136
Issue Date:: 2016-10-01

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Field	Value	Language
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Kong, X	en_US
dc.contributor.author	Zheng, L https://orcid.org/0000-0002-1464-9500	en_US
dc.contributor.author	Tian, Q	en_US
dc.date.issued	2016-10-01	en_US
dc.identifier.citation	MM 2016 - Proceedings of the 2016 ACM Multimedia Conference, 2016, pp. 132 - 136	en_US
dc.identifier.isbn	9781450336031	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120167
dc.description.abstract	© 2016 ACM. The Convolutional Neural Networks (CNNs) have achieved breakthroughs on several image retrieval benchmarks. Most previous works re-formulate CNNs as global feature extractors used for linear scan. This paper proposes a Multilayer Orderless Fusion (MOF) approach to integrate the activations of CNN in the Bag-of-Words (BoW) framework. Specifically, through only one forward pass in the network, we extract multi-layer CNN activations of local patches. Activations from each layer are aggregated in one BoW model, and several BoW models are combined with late fusion. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed method.	en_US
dc.relation.ispartof	MM 2016 - Proceedings of the 2016 ACM Multimedia Conference	en_US
dc.relation.isbasedon	10.1145/2964284.2967197	en_US
dc.title	Exploiting hierarchical activations of neural network for image retrieval	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 ACM. The Convolutional Neural Networks (CNNs) have achieved breakthroughs on several image retrieval benchmarks. Most previous works re-formulate CNNs as global feature extractors used for linear scan. This paper proposes a Multilayer Orderless Fusion (MOF) approach to integrate the activations of CNN in the Bag-of-Words (BoW) framework. Specifically, through only one forward pass in the network, we extract multi-layer CNN activations of local patches. Activations from each layer are aggregated in one BoW model, and several BoW models are combined with late fusion. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed method.

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http://hdl.handle.net/10453/120167