NDLI: Learning people annotation from the web via consistency learning

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Learning people annotation from the web via consistency learning

Content Provider	ACM Digital Library
Author	Islam, Atiq Yagnik, Jay
Abstract	The phenomenal growth of Image/Video on the web and the increasing sparseness of meta information to go along with forces us to look for signals from the Image/Video content for Search / Information Retrieval and Browsing based corpus exploration. One of the prominent type of information that users look for while searching/browsing through such corpora is information around the people present in the Image/Video. While face recognition has matured to some extent over the past few years, this problem remains a hard one due to a) absence of labelled data for such a large set of celebrities that users look for and b) the variability of age/makeup/expressions/pose in the target corpus. We propose a learning paradigm which we refer to as consistency learning to address both these issues by posing the problem of learning from weakly labelled training set. We use the text-image co-occurrence on the web as a weak signal of relevance and learn the set of consistent face models from this very large and noisy training set. The resulting system learns face models for a large set of celebrities directly from the web and uses it to tag Image/Video for better retrieval. While the proposed method has been applied to faces, we see it broadly applicable in any learning problem with a suitable similarity metric defined. We present results on learning from a very large dataset of 37 million images resulting in a validation accuracy of 92.68%.
Starting Page	285
Ending Page	290
Page Count	6
File Format	PDF
ISBN	9781595937780
DOI	10.1145/1290082.1290121
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-09-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Face recognition Correlation sampling Graph sampling Consistency learning Learning from noisy training sets
Content Type	Text
Resource Type	Article

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