Paper Info
Title
Automatic visual features weights obtention for Content-Based Image Retrieval Systems
Abstract
In Content-Based Image Retrieval (CBIR) Systems it is necessary to combine more than one visual descriptor in order to improve the retrieval performance. The most common descriptors are Color-Based, Shape-Based and Texture-Based descriptors. When more than one visual descriptor is linearly combined, some adequate weight must be assigned to each visual feature. The most common manner is setting the same weight value for each visual feature. The sum of these values must be equal to one. However, this process does not guarantee the optimum performance of the CBIR system. In order to guarantee the best performance, it is necessary to do several experimentations to find the optimum weight values combination. This is time consuming process and ambiguous, due to the weights values depends on the nature of the databases. In this paper we proposed a scheme which computes automatically the best weight combination and guarantees the optimum performance of the CBIR system.
Year
DOI
Venue
2015
10.1109/ICEEE.2015.7357918
2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
Keywords
Field
DocType
CBIR,Visual Descriptors,Weighted Linear Combination,Weighted Visual Features
Computer vision,Automatic image annotation,Pattern recognition,Image texture,Visualization,Computer science,Image retrieval,Feature extraction,Artificial intelligence,Content-based image retrieval,Visual Word
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
6
Name
Order
Citations
PageRank
Atoany N. Fierro-Radilla121.44
Karina Toscano-Medina2198.94
Mariko Nakano-Miyatake314429.99
Héctor Pérez-Meana44815.66
Manuel Cedillo-Hernandez56810.06
Francisco García-Ugalde6338.12