Binli M.K.Can Demiryilmaz B.Ekim P.O.Yeganli F.2023-06-162023-06-1620199.79E+12https://doi.org/10.23919/ELECO47770.2019.8990524https://hdl.handle.net/20.500.14365/372511th International Conference on Electrical and Electronics Engineering, ELECO 2019 -- 28 November 2019 through 30 November 2019 -- 157784This work employs a new method of feature selection and classification of image objects by combining previous studies in literature of feature selection and classification of images. In the new algorithm, instead of sliding a window on the image and scaling the window by applying normalized cuts and image segmentation algorithms, information related to the position of objects is considered. Accordingly, the scaling of searching image window process is been exceeded. For this purpose, the obtained segments of image features extracted by employing Histogram Oriented Gradient (HOG) descriptor, which clears the images from the curse of dimensions. These extracted features optimized by applying Genetic Algorithm (GA) method, to detect faces and compared with HOG feature results. The main objective of this work is to improve the accuracy of Support Vector Machine (SVM) classifier in detail description applications. © 2019 Chamber of Turkish Electrical Engineers.eninfo:eu-repo/semantics/closedAccessClassification (of information)Face recognitionFeature extractionGenetic algorithmsImage classificationSupport vector machinesDescriptorsFeature selection and classificationImage featuresImage objectsImage segmentation algorithmImage windowNormalized cutsOriented gradientsImage segmentationConference Object10.23919/ELECO47770.2019.89905242-s2.0-85080899566