Modeling annotated data

Abstract

We consider the problem of modeling annotated data---data with multiple types where the instance of one type (such as a caption) serves as a description of the other type (such as an image). We describe three hierarchical probabilistic mixture models which aim to describe such data, culminating in <i>correspondence latent Dirichlet allocation</i>, a latent variable model that is effective at modeling the joint distribution of both types and the conditional distribution of the annotation given the primary type. We conduct experiments on the Corel database of images and captions, assessing performance in terms of held-out likelihood, automatic annotation, and text-based image retrieval.

DOI: 10.1145/860435.860460

Extracted Key Phrases

8 Figures and Tables

Unfortunately, ACM prohibits us from displaying non-influential references for this paper.

To see the full reference list, please visit http://dl.acm.org/citation.cfm?id=860460.

Showing 1-10 of 656 extracted citations
050100'03'05'07'09'11'13'15'17
Citations per Year

1,082 Citations

Semantic Scholar estimates that this publication has received between 951 and 1,233 citations based on the available data.

See our FAQ for additional information.