Social multimedia sharing and hosting websites,
such as Flickr and Facebook, contain billions of user-submitted
images. Popular Internet commerce websites such as
Amazon.com are also furnished with tremendous amounts of
product-related images. In addition, images in such social
networks are also accompanied by annotations, comments, and
other information, thus forming heterogeneous image-rich
information networks. In this paper, the concept of
(heterogeneous) image-rich information network and the
problem of how to perform information retrieval and
recommendation in such networks is introduced. A fast
algorithm, heterogeneous minimum order k-SimRank (HMok-
SimRank) is proposed to compute link-based similarity in
weighted heterogeneous information networks. Then, we
propose an algorithm Integrated Weighted Similarity Learning
(IWSL) to account for both link-based and content based
similarities by considering the network structure and mutually
reinforcing link similarity and feature weight learning.
Ashwini Jaid : Computer Department, Siddhant College of Engineering, Pune, Maharashtra, India
Komal Savant : Computer Department, Siddhant College of Engineering, Pune, Maharashtra, India
Sonali Varma : Computer Department, Siddhant College of Engineering, Pune, Maharashtra, India
Pushpa Jat : Computer Department, Siddhant College of Engineering, Pune, Maharashtra, India
Sushama Shinde : Assistant Professor, Computer Department, Siddhant College of Engineering, Pune, Maharashtra, India
Information Retrieval
Image Mining
Information
Network
Ranking.
In this paper efficient way of finding similar objects (such
as photos and products) is presented by modeling major
social sharing and e-commerce websites as image rich
information networks. The algorithm minimum order SimRank is proposed which efficiently computes weighted
link-based similarity in weighted heterogeneous imagerich
information networks. In future, under the concept of
heterogeneous image rich information network, the study
can be performed how such kind of network structure may
benefit various image mining and computer vision tasks,
such as image categorization, image segmentation, tag
annotation, and collaborative filtering.
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