Relevance Feedback in Multimedia Databases (2003)
| Venue: | In Handbook of Video Databases: Design and Applications |
| Citations: | 9 - 0 self |
BibTeX
@INPROCEEDINGS{Ortega-Binderberger03relevancefeedback,
author = {Michael Ortega-Binderberger and Sharad Mehrotra},
title = {Relevance Feedback in Multimedia Databases},
booktitle = {In Handbook of Video Databases: Design and Applications},
year = {2003},
publisher = {CRC Press}
}
Years of Citing Articles
OpenURL
Abstract
INTRODUCTION The popularity of web search engines has familiarized countless users with the similarity search paradigm. In this paradigm a user provides an example or simple sketch of desired information to a system and receives a list of items that "best" match the information provided. These results are typically sorted by a system-generated estimate of how closely they match the sketch/requirement provided by users. Consider a typical web search engine. The users' sketch takes the form of keywords and the search engine finds the web pages that best match those keywords. User expectations have grown to demand powerful and flexible search capabilities for multimedia data such as images and video in addition to the traditional unstructured web pages. Consider a user searching for pictures depicting a "sunset by the sea" in an image database. One possibility is to attach a text description to each image and use standard text search engine techniques to find the results. The proble







