Web image prediction using multivariate point processes

  • Authors:

  • Gunhee Kim;Li Fei-Fei;Eric P. Xing

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA, USA;Stanford University, Stanford, CA, USA;Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2012

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Abstract

In this paper, we investigate a problem of predicting what images are likely to appear on the Web at a future time point, given a query word and a database of historical image streams that potentiates learning of uploading patterns of previous user images and associated metadata. We address such a Web image prediction problem at both a collective group level and an individual user level. We develop a predictive framework based on the multivariate point process, which employs a stochastic parametric model to solve the relations between image occurrence and the covariates that influence it, in a flexible, scalable, and globally optimal way. Using Flickr datasets of more than ten million images of 40 topics, our empirical results show that the proposed algorithm is more successful in predicting unseen Web images than other candidate methods, including forecasting on semantic meanings only, a PageRank-based image retrieval, and a generative author-time topic model.