2009 International Conference on Information Technology and Computer Science 2009
DOI: 10.1109/itcs.2009.321
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Cited by 5 publications
(10 citation statements)
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“…We design experiments on ImageCLEFmed and medical image data set used by Wu et al (2009), which will answer the following questions:…”
Section: Methodsmentioning
confidence: 99%
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“…We design experiments on ImageCLEFmed and medical image data set used by Wu et al (2009), which will answer the following questions:…”
Section: Methodsmentioning
confidence: 99%
“…In this section, we will first introduce the ImageCLEFmed medical image data set and retrieval accuracy metric briefly in Section 4.1, and then we compare ILMDA to the state-of-the-art dimensionality reduction methods using a subset of ImageCLEFmed in Section 4.2, at last we will see how our dimensionality reduction methods work with different image features and retrieval schemes, using both the two data sets: ImageCLEFmed and Wu et al (2009) It currently contains 50,026 arbitrarily selected anonymous radiographic images taken randomly from patients of different ages, genders, and pathologies during medical routine. The images are categorised into 116 classes according to the IRMA code (Lehmann et al, 2003).…”
Section: Methodsmentioning
confidence: 99%
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“…Researchers have addressed the dimensionality reduction problem by applying various algorithms such as Principal Component Analysis, Weighted Multi-Dimensional Scaling, Tabu Search Method (Wu et al, 2009) and Evolutionary Algorithms for optimizing the features are Particle Swarm Optimization (PSO) (Ye et al, 2009), Ant Colony Optimization (ACO) (Piatrik and Izquierdo, 2009), Genetic Algorithms (GA) (Silva et al, 2011), Gravitational Search Algorithm (GSA) (Rashedi et al, 2009). The individual dimensional reduction approach based CBMIR is used to recognize the probable optimal resolutions within a sensible amount of time but the convergence rapidity is declined.…”
Section: Level 2: Optimized Feature Selectionmentioning
confidence: 99%