NDLI: Tagging Personal Photos with Transfer Deep Learning

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Tagging Personal Photos with Transfer Deep Learning

Content Provider	ACM Digital Library
Author	Yang, Kuiyuan Fu, Jianlong Mei, Tao Lu, Hanqing Rui, Yong
Abstract	The advent of mobile devices and media cloud services has led to the unprecedented growing of personal photo collections. One of the fundamental problems in managing the increasing number of photos is automatic image tagging. Existing research has predominantly focused on tagging general Web images with a well-labelled image database, e.g., ImageNet. However, they can only achieve limited success on personal photos due to the domain gaps between personal photos and Web images. These gaps originate from the differences in semantic distribution and visual appearance. To deal with these challenges, in this paper, we present a novel transfer deep learning approach to tag personal photos. Specifically, to solve the semantic distribution gap, we have designed an ontology consisting of a hierarchical vocabulary tailored for personal photos. This ontology is mined from \$10,000\$ active users in Flickr with 20 million photos and 2.7 million unique tags. To deal with the visual appearance gap, we discover the intermediate image representations and ontology priors by deep learning with bottom-up and top-down transfers across two domains, where Web images are the source domain and personal photos are the target. Moreover, we present two modes (single and batch-modes) in tagging and find that the batch-mode is highly effective to tag photo collections. We conducted personal photo tagging on 7,000 real personal photos and personal photo search on the MIT-Adobe FiveK photo dataset. The proposed tagging approach is able to achieve a performance gain of \$12.8\%\$ and \$4.5\%\$ in terms of NDCG@5, against the state-of-the-art hand-crafted feature-based and deep learning-based methods, respectively.
Starting Page	344
Ending Page	354
Page Count	11
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741112
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Deep learning Personal photo Ontology Transfer learning Image tagging
Content Type	Text
Resource Type	Article

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