Tasci, ErdalUgur, Aybars2023-01-122023-01-1220221380-75011573-77211380-75011573-7721https://doi.org/10.1007/s11042-022-12909-whttps://hdl.handle.net/11454/76176Nowadays, with the advances and use of technological possibilities and devices, the number of digital images is increasing gradually. Computer-aided classification of image types is widely applied in many applications such as medicine, security, and automation. The feature extraction and selection stages have great importance in terms of improving the classification performance as sub-stages of the pattern recognition process. Researchers apply different feature extraction methods for their works due to the requirements. In this study, a novel pattern recognition framework combining diverse and large-scale handcrafted feature extraction methods (shape-based and texture-based) and the selection stage on images is developed. Genetic algorithms are also used for feature selection. In the experimental studies, Flavia leaf recognition, Caltech101 object classification image datasets, and five supervised classification models (random forest, ECOC-SVM, k-nearest neighbor, AdaBoost, classification tree) with different parameters' values are used. The experimental results show that the proposed method achieves 98.39% and 82.77% accuracy rates on Flavia and Caltech101 datasets with the ECOC-SVM model, respectively. The proposed framework is also competitive with the existing state-of-the-art methods in the related literature.en10.1007/s11042-022-12909-winfo:eu-repo/semantics/closedAccessPattern recognitionImage processingFeature extractionFeature selectionMachine learningClassificationShapeAlgorithmsArticle81213019530218WOS:0007790455000022-s2.0-85127613433Q1Q2