Paper Info
Title
A naive relevance feedback model for content-based image retrieval using multiple similarity measures
Abstract
This paper presents a novel probabilistic framework to process multiple sample queries in content based image retrieval (CBIR). This framework is independent from the underlying distance or (dis)similarity measures which support the retrieval system, and only assumes mutual independence among their outcomes. The proposed framework gives rise to a relevance feedback mechanism in which positive and negative data are combined in order to optimally retrieve images according to the available information. A particular setting in which users interactively supply feedback and iteratively retrieve images is set both to model the system and to perform some objective performance measures. Several repositories using different image descriptors and corresponding similarity measures have been considered for benchmarking purposes. The results have been compared to those obtained with other representative strategies, suggesting that a significant improvement in performance can be obtained.
Year
DOI
Venue
2010
10.1016/j.patcog.2009.08.010
Pattern Recognition
Keywords
Field
DocType
naive relevance feedback model,retrieval system,different image descriptors,relevance feedback mechanism,corresponding similarity measure,proposed framework,image retrieval,objective performance measure,multiple similarity measure,similarity measure,users interactively supply feedback,content-based image retrieval,novel probabilistic framework
Data mining,Similitude,Relevance feedback,Iterative method,Image retrieval,Visual descriptors,Artificial intelligence,Machine learning,Mathematics,Benchmarking,Content-based image retrieval,Probabilistic framework
Journal
Volume
Issue
ISSN
43
3
Pattern Recognition
Citations 
PageRank 
References 
26
0.80
35
Authors
3
Name
Order
Citations
PageRank
Miguel Arevalillo-Herráez121026.08
Francesc J. Ferri235638.92
Juan Domingo33319258.54