Long-term relevance feedback and feature selection for adaptive content based image suggestion

Abstract

Content-based image suggestion (CBIS) addresses the satisfaction of users long-term needs for ‘‘relevant’’ and ‘‘novel’’ images. In this paper, we present VCC-FMM, a flexible mixture model that clusters both images and users into separate groups. Then, we propose long-term relevance feedback to maintain accurate modeling of growing image collections and changing user long-term needs over time. Experiments on a real data set show merits of our approach in terms of image suggestion accuracy and efficiency. & 2010 Elsevier Ltd. All rights reserved.

DOI: 10.1016/j.patcog.2010.06.003

Cite this paper

@article{Boutemedjet2010LongtermRF, title={Long-term relevance feedback and feature selection for adaptive content based image suggestion}, author={Sabri Boutemedjet and Djemel Ziou}, journal={Pattern Recognition}, year={2010}, volume={43}, pages={3925-3937} }