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In this paper, support vector machine (SVM) and mixed gravitational search algorithm (MGSA) are utilized to detect the breast cancer tumors in mammography images. Sech template matching method is used to segment images and extract the regions of interest (ROIs). Gray-level co-occurrence matrix (GLCM) is used to extract features. The mixed GSA is used for optimization of the classifier parameters and...
In this paper, artificial neural network (ANN) and improved binary gravitational search algorithm (IBGSA) are utilized to detect objects in images. Watershed algorithm is used to segment images and extract the objects. Color, texture and geometric features are extracted from each object. IBGSA is used as a feature selection method to find the best subset of features for classifying the desired objects...
In content-based image retrieval (CBIR) applications, each database needs its corresponding parameter setting for feature extraction. However, most of the CBIR systems perform indexing by a set of fixed and pre-specific parameters. On the other hand, feature selection methods have currently gained considerable popularity to reduce semantic gap. In this regard, this paper is devoted to present a hybrid...
In this paper, feature selection using binary gravitational search algorithm is utilized to improve the precision of CBIR systems. Content-based image retrieval, CBIR, is one of the most challenging problems in the field of pattern recognition. The performance of a CBIR system is hardly depends on the features that are extracted from images. Thus, selecting most relevant features leads to higher accuracy...
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