@inproceedings{cde13d124f144db89bdef387f827ffbf,
title = "Incorporating real-valued multiple instance learning into relevance feedback for image retrieval",
abstract = "This paper presents a content-based image retrieval (CBIR) system that incorporates real-valued multiple instance learning (MIL) into the user relevance feedback (RF) to learn the user's subjective visual concepts, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of the user. RF provides a way to obtain the subjectivity of the user's high-level visual concepts, and MIL enables the automatic learning of the user's high-level concepts. The user interacts with the CBIR system by relevance feedback in a way that the extent to which the image samples retrieved by the system are relevant to the user's intention is labeled. The system in turn applies the MIL method to find user's most interested image region from the feedback. A multilayer neural network that is trained progressively through the feedback and learning procedure is used to map the low-level image features to the high-level concepts.",
author = "Xin Hunag and Chen, {Shu Ching} and Shyu, {Mei Ling}",
year = "2003",
month = jan,
day = "1",
doi = "10.1109/ICME.2003.1220919",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
pages = "I321--I324",
booktitle = "Proceedings - 2003 International Conference on Multimedia and Expo, ICME",
note = "2003 International Conference on Multimedia and Expo, ICME 2003 ; Conference date: 06-07-2003 Through 09-07-2003",
}