iRIN: image retrieval in image-rich information networks

Authors:
Xin Jin;Jiebo Luo;Jie Yu;Gang Wang;Dhiraj Joshi;Jiawei Han
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL, USA;Kodak Research Laboratories, Eastman Kodak Company, Rochester, NY, USA;Kodak Research Laboratories, Eastman Kodak Company, Rochester, NY, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA;Kodak Research Laboratories, Eastman Kodak Company, Rochester, NY, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA
Venue:
Proceedings of the 19th international conference on World wide web
Year:
2010

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Abstract

In this demo, we present a system called iRIN designed for performing image retrieval in image-rich information networks. We first introduce MoK-SimRank to significantly improve the speed of SimRank, one of the most popular algorithms for computing node similarity in information networks. Next, we propose an algorithm called SimLearn to (1) extend MoK-SimRank to heterogeneous image-rich information network, and (2) account for both link-based and content-based similarities by seamlessly integrating reinforcement learning with feature learning.