NDLI: Clustering Social Event Images Using Kernel Canonical Correlation Analysis

Content Provider	IEEE Xplore Digital Library
Author	Ahsan, U. Essa, I.
Copyright Year	2014
Description	Author affiliation: Georgia Inst. of Technol., Atlanta, GA, USA (Ahsan, U.; Essa, I.)
Abstract	Sharing user experiences in form of photographs, tweets, text, audio and/or video has become commonplace in social networking websites. Browsing through large collections of social multimedia remains a cumbersome task. It requires a user to initiate textual search query and manually go through a list of resulting images to find relevant information. We propose an automatic clustering algorithm, which, given a large collection of images, groups them into clusters of different events using the image features and related metadata. We formulate this problem as a kernel canonical correlation clustering problem in which data samples from different modalities or 'views' are projected to a space where correlations between the samples projections are maximized. Our approach enables us to learn a semantic representation of potentially uncorrelated feature sets and this representation is clustered to give unique social events. Furthermore, we leverage the rich information associated with each uploaded image (such as usernames, dates/timestamps, etc.) and empirically determine which combination of feature sets yields the best clustering score for a dataset of 100, 000 images.
Sponsorship	IEEE Comput. Soc.
Starting Page	814
Ending Page	819
File Size	534312
Page Count	6
File Format	PDF
ISBN	9781479943081
ISSN	21607516
DOI	10.1109/CVPRW.2014.124
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Correlation Clustering algorithms social multimedia Vectors Multimedia communication machine learning Kernel image clustering Standards
Content Type	Text
Resource Type	Article

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