Paper Info
Title
Including the perceptual parameter to tune the retrieval ability of pulmonary CBIR systems
Abstract
The research on Content-Based Image Retrieval (CBIR) is growing in relevance at a fast pace. Algorithms and tools for CBIR can help decision-making processes, for example allowing the specialist to retrieve cases similar,,to the one under evaluation. However, the main reservation about using CBIR is the semantic gap, which is the divergence among automatic results and what the user is expecting.We propose the "perceptual parameter", which allows changing the relationship between the feature extraction algorithms and the distance functions, aimed at finding the best integration of both from the specialist's point of view. This work integrates the three main elements of similarity queries: the extracted features from the images, the distance function employed to quantify the similarity and the similarity perception from the user. These three elements allowed to build the "similarity operators". The experiments performed show that the new perceptual parameter can narrow the semantic gap between what the system retrieves and what the specialist expects.
Year
DOI
Venue
2009
10.1109/CBMS.2009.5255399
CBMS
Keywords
Field
DocType
content-based retrieval,feature extraction,image retrieval,medical image processing,content-based image retrieval,decision-making process,distance function,feature extraction algorithm,perceptual parameter,pulmonary CBIR system,semantic gap,similarity perception,similarity query
Histogram,Computer science,Metric (mathematics),Image retrieval,Artificial intelligence,Computer vision,Information retrieval,Pattern recognition,Visualization,Semantic gap,Feature extraction,Perception,Content-based image retrieval
Conference
ISSN
Citations 
PageRank 
2372-9198
6
0.47
References 
Authors
4
5
Name
Order
Citations
PageRank
Marcelo Ponciano-Silva1384.62
Agma J. M. Traina21024153.61
Paulo M. Azevedo-Marques319519.04
Joaquim Cezar Felipe4547.17
Caetano Traina Jr.51052137.26