Automatic visual features weights obtention for Content-Based Image Retrieval Systems

Research output: Contribution to conferencePaper

2 Scopus citations

Abstract

© 2015 IEEE. 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.
Original languageAmerican English
DOIs
StatePublished - 14 Dec 2015
Event2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2015 -
Duration: 14 Dec 2015 → …

Conference

Conference2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2015
Period14/12/15 → …

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    Fierro-Radilla, A., Toscano-Medina, K., Nakano-Miyatake, M., Perez-Meana, H., Cedillo-Hernandez, M., & Garcia-Ugalde, F. (2015). Automatic visual features weights obtention for Content-Based Image Retrieval Systems. Paper presented at 2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2015, . https://doi.org/10.1109/ICEEE.2015.7357918