Multi-Modal Image Annotation with Multi-Instance Multi-Label LDA
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1558-1563.
Abstract
This paper studies the problem of image annotation in a multi-modal setting where both visual and textual information are available. We propose Multi-modal Multi-instance Multi-label Latent Dirichlet Allocation (M3LDA), where the model consists of a visual-label part, a textual-label part and a label-topic part. The basic idea is that the topic decided by the visual information and the topic decided by the textual information should be consistent, leading to the correct label assignment. Particularly, M3LDA is able to annotate image regions, thus provides a promising way to understand the relation between input patterns and output semantics. Experiments on Corel5K and ImageCLEF validate the effectiveness of the proposed method.
@inproceedings{ijcai2013p232,
title = {Multi-Modal Image Annotation with Multi-Instance Multi-Label LDA},
author = {Nguyen, Cam-Tu and Zhan, De-Chuan and Zhou, Zhi-Hua},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {1558--1563},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/232},
}
TY - CPAPER TI - Multi-Modal Image Annotation with Multi-Instance Multi-Label LDA AU - Nguyen, Cam-Tu AU - Zhan, De-Chuan AU - Zhou, Zhi-Hua T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 1558 EP - 1563 UR - https://ijcai.org/proceedings/2013/232 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
