@InProceedings{PedronetteTorr:2013:UnMeEs,
author = "Pedronette, Daniel Carlos Guimar{\~a}es and Torres, Ricardo da
S.",
affiliation = "{State University of S{\~a}o Paulo (UNESP)} and {University of
Campinas (UNICAMP)}",
title = "Unsupervised measures for estimating the effectiveness of image
retrieval systems",
booktitle = "Proceedings...",
year = "2013",
editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva,
Claudio",
organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "effectiveness estimation, content-based image retrieval, rank
aggregation.",
abstract = "The main objective of Content-Based Image Retrieval (CBIR) systems
is to retrieve a ranked list containing the most similar images of
a collection given a query image, by taking into account their
visual content. Although these systems represent a very promising
approach, in many situations is very challenging to assure the
quality of returned ranked lists. Supervised approaches rely on
training data and information obtained from user interactions to
identify and then improve low-quality results. However, these
approaches require a lot of human efforts which can be infeasible
for many systems. In this paper, we present two novel unsupervised
measures for estimating the effectiveness of ranked lists in CBIR
tasks. Given an estimation of the effectiveness of ranked lists,
many CBIR systems can, for example, emulate the training process,
but now without any user intervention. Improvements can also be
achieved on several unsupervised approaches, such as re-ranking
and rank aggregation methods, once the estimation measures can
help to consider more relevant information by distinguishing
effective from non-effective ranked lists. Both proposed measures
are computed using a novel image representation of ranked lists
and distances among images considering a given dataset. The
objective is to exploit the visual patterns encoded in the image
representations for estimating the effectiveness of ranked lists.
Experiments involving shape, color, and texture descriptors
demonstrate that the proposed approaches can provide accurate
estimations of the quality in terms of effectiveness of ranked
lists. The use of proposed measures are also evaluated in image
retrieval tasks aiming at improving the effectiveness of rank
aggregation approaches.",
conference-location = "Arequipa, Peru",
conference-year = "5-8 Aug. 2013",
doi = "10.1109/SIBGRAPI.2013.54",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2013.54",
language = "en",
ibi = "8JMKD3MGPBW34M/3EDL7SB",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3EDL7SB",
targetfile = "PID2848405.pdf",
urlaccessdate = "2024, May 02"
}