@inproceedings{27f2370b056449cbb34dc7394c7f2817,
title = "A novel image retrieval model",
abstract = "Image retrieval is the hot point of researchers in many domains. Traditional text-based query methods use caption and keywords to annotate and retrieval image databases, which often consumes a mass of human labor. Feature vector based retrieval methods only can provide the query by example, and can't provide retrieval on semantic level. In this paper, we propose a novel image retrieval model that combines good qualities of those two methods above-mentioned. It utilizes the image low-level features and the user relevance feedback mechanism to classify images and acquire high-level semantic information. Furthermore, the image classification and the semantic information are not fixed, which can be changed by the user according to his preference. Experiments show that our scheme can achieve high efficiency.",
keywords = "Image classification, Image retrieval, Relevance feedback mechanism, Semantic information",
author = "Han, \{Jun Wei\} and Lei Guo and Bao, \{Yong Sheng\}",
note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; 6th International Conference on Signal Processing, ICSP 2002 ; Conference date: 26-08-2002 Through 30-08-2002",
year = "2002",
doi = "10.1109/ICOSP.2002.1179945",
language = "英语",
series = "International Conference on Signal Processing Proceedings, ICSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "953--956",
editor = "Xiaofang Tang and Baozong Yuan",
booktitle = "ICSP 2002 - 2002 6th International Conference on Signal Processing, Proceedings",
}