Collection-based sparse label propagation and its application on social group suggestion from photos

  • Authors:

  • Jie Yu;Xin Jin;Jiawei Han;Jiebo Luo

  • Affiliations:

  • General Electric Company, Niskayuna, NY;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;Eastman Kodak Company, New York, NY

  • Venue:
  • ACM Transactions on Intelligent Systems and Technology (TIST)
  • Year:
  • 2011

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Abstract

Online social network services pose great opportunities and challenges for many research areas. In multimedia content analysis, automatic social group recommendation for images holds the promise to expand one's social network through media sharing. However, most existing techniques cannot generate satisfactory social group suggestions when the images are classified independently. In this article, we present novel methods to produce accurate suggestions of suitable social groups from a user's personal photo collection. First, an automatic clustering process is designed to estimate the group similarities, select the optimal number of clusters and categorize the social groups. Both visual content and textual annotations are integrated to generate initial predictions of the group categories for the images. Next, the relationship among images in a user's collection is modeled as a sparse graph. A collection-based sparse label propagation method is proposed to improve the group suggestions. Furthermore, the sparse graph-based collection model can be readily exploited to select the most influential and informative samples for active relevance feedback, which can be integrated with the label propagation process without the need for classifier retraining. The proposed methods have been tested on group suggestion tasks for real user collections and demonstrated superior performance over the state-of-the-art techniques.