Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2018/r/munzur/5262
  Classification of Human Actions in Outdoor Images Using Hybrid Deep Learning Methods

  Tümen, Vedat; Algur, Özlem; Yıldırım, Özal

  In this paper, a hybrid deep learning approach is proposed for automatic classification of human actions in outdoor images. Firstly, the detection of the humans in the image taken from the outdoor environment is provided. For this purpose, YOLO, a pre-trained deep object detection tool widely used in the literature, is used. The Google street view platform was preferred to acquire outdoor images. Action classes were created for the detected humans. These action classes; walking to the right, walking to the left, standing and sitting. Thus, a comprehensive dataset has been created for humans identified from the outdoor images. A convolutional neural network (CNN) model is designed for the automatic recognition of classes. With this designed CNN model, automatic recognition of the human actions is provided. This trained model is used in a hybrid manner with the YOLO object detection system to automatically recognize the human actions in the input images.Within the scope of the article, a four-class dataset was created and performance evaluations of the proposed system were made.

  AtıfTR Dizin1

  İncele
• BildiriAçık Erişim2017/r/munzur/670
  Facial emotion recognition on a dataset using Convolutional Neural Network

  Tümen, Vedat; Söylemez, Ömer Faruk; Ergen, Burhan

  Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.

  AtıfScopus68·OpenAlex83

  İncele
• MakaleAçık Erişim2015Scopus SJRQ2/r/munzur/4187
  Automatic classification of harmonic data using k-means and least square support vector machine

  Eristi, Huseyin; Tümen, Vedat; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup

  In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using k-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a k-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the k-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.

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  AtıfWoS1·OpenAlex3

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