TY - GEN
T1 - Automatic visual features weights obtention for Content-Based Image Retrieval Systems
AU - Fierro-Radilla, Atoany
AU - Toscano-Medina, Karina
AU - Nakano-Miyatake, Mariko
AU - Perez-Meana, Hector
AU - Cedillo-Hernandez, Manuel
AU - Garcia-Ugalde, Francisco
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/14
Y1 - 2015/12/14
N2 - 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.
AB - 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.
KW - CBIR
KW - Visual Descriptors
KW - Weighted Linear Combination
KW - Weighted Visual Features
UR - http://www.scopus.com/inward/record.url?scp=84962840699&partnerID=8YFLogxK
U2 - 10.1109/ICEEE.2015.7357918
DO - 10.1109/ICEEE.2015.7357918
M3 - Contribución a la conferencia
AN - SCOPUS:84962840699
T3 - 2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2015
BT - 2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2015
Y2 - 26 October 2015 through 30 October 2015
ER -