@inproceedings{a164fe9f55ea40b69b7796f490a26ebb,
title = "Adaptive multi-class metric content-based image retrieval",
abstract = "Relevance feedback methods for content-based image retrieval have shown promise in a variety of image database applications. These techniques assume two (relevant and irrelevant) class relevance feedback. While simple computationally, two class relevance feedback often becomes inadequate in providing su{\AE}cient information to help rapidly improve retrieval performance. We propose a locally adaptive technique for content-based image retrieval that enables relevance feedback to take on multi-class form. We estimate a exible multi-class metric for computing retrievals based on Chi-squared distance analysis. As a result, local data distributions can be su{\AE}ciently exploited, whereby rapid performance improvement can be achieved. The e{\AE}cacy of our method is validated and compared against other competing techniques using a number of real world data sets.",
author = "Jing Peng",
year = "2000",
month = jan,
day = "1",
doi = "10.1007/3-540-40053-2_36",
language = "English",
isbn = "3540411771",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "407--418",
editor = "Robert Laurini",
booktitle = "Advances in Visual Information Systems - 4th International Conference, VISUAL 2000, Proceedings",
note = "null ; Conference date: 02-11-2000 Through 04-11-2000",
}