2012 WebImagePredictionUsingMultivar

• (Kim et al., 2012) ⇒ Gunhee Kim, Li Fei-Fei, and Eric P. Xing. (2012). “Web Image Prediction Using Multivariate Point Processes.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339699

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Web+Image+Prediction+Using+Multivariate+Point+Processes
• http://dl.acm.org/citation.cfm?id=2339530.2339699&preflayout=flat#citedby

Quotes

Author Keywords

• Multivariate point processes; penalized poisson regression; personalization; retrieval models; web image prediction

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 WebImagePredictionUsingMultivar	Eric P. Xing Li Fei-Fei Gunhee Kim			Web Image Prediction Using Multivariate Point Processes				10.1145/2339530.2339699		2012