@inproceedings{6541f6c0103c4df5a3a4af1e451f04e3,
title = "Adaptive relevance feedback for fusion of text and visual features",
abstract = "It has been shown that query can be correlated with its context to a different extent; in this case the feedback images. We introduce an adaptive weighting scheme where the respective weights are automatically modified, depending on the relationship strength between visual query and its visual context and textual query and its textual context; the number of terms or visual terms (mid-level visual features) co-occurring between current query and its context. The user simulation experiment has shown that this kind of adaptation can indeed further improve the effectiveness of hybrid CBIR models.",
keywords = "Adaptive Weighting Scheme, Early Fusion, Hybrid Relevance Feedback, Late Fusion, Re-Ranking, Textual Features, Visual Features",
author = "Leszek Kaliciak and Hans Myrhaug and Ayse Goker and Dawei Song",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 18th International Conference on Information Fusion, Fusion 2015 ; Conference date: 06-07-2015 Through 09-07-2015",
year = "2015",
month = sep,
day = "14",
language = "English",
series = "2015 18th International Conference on Information Fusion, Fusion 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1322--1329",
booktitle = "2015 18th International Conference on Information Fusion, Fusion 2015",
address = "United States",
}