DocumentCode :
254766
Title :
Clustering Social Event Images Using Kernel Canonical Correlation Analysis
Author :
Ahsan, Unaiza ; Essa, I.
Author_Institution :
Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2014
fDate :
23-28 June 2014
Firstpage :
814
Lastpage :
819
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.
Keywords :
correlation methods; image representation; meta data; pattern clustering; social networking (online); automatic clustering algorithm; image features; kernel canonical correlation analysis; kernel canonical correlation clustering problem; metadata; semantic representation; social event images clustering; social multimedia; social networking Websites; Clustering algorithms; Correlation; Kernel; Multimedia communication; Standards; Vectors; Visualization; image clustering; machine learning; social multimedia;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on
Conference_Location :
Columbus, OH
Type :
conf
DOI :
10.1109/CVPRW.2014.124
Filename :
6910075
Link To Document :
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