A Probabilistic Framework for Multimodal Retrieval Using Integrative Indian Buffet Process
Abstract
We propose a multimodal retrieval procedure based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. Experiments on two multimodal datasets, PASCAL-Sentence and SUN-Attribute, demonstrate the effectiveness of the proposed retrieval procedure in comparison to the state-of-the-art algorithms for learning binary codes.
Cite
Text
Ozdemir and Davis. "A Probabilistic Framework for Multimodal Retrieval Using Integrative Indian Buffet Process." Neural Information Processing Systems, 2014.Markdown
[Ozdemir and Davis. "A Probabilistic Framework for Multimodal Retrieval Using Integrative Indian Buffet Process." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/ozdemir2014neurips-probabilistic/)BibTeX
@inproceedings{ozdemir2014neurips-probabilistic,
title = {{A Probabilistic Framework for Multimodal Retrieval Using Integrative Indian Buffet Process}},
author = {Ozdemir, Bahadir and Davis, Larry S.},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {2384-2392},
url = {https://mlanthology.org/neurips/2014/ozdemir2014neurips-probabilistic/}
}