Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Ergun, Hilalsearch.filters.applied.f.language: search.filters.language.tur

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    Video Scene Classification Using Spatıal Pyramid Based Features
    (2014) Sert, Mustafa; Ergun, Hilal; https://orcid.org/0000-0002-7056-4245; AAB-8673-2019
    Recognition of video scenes is a challenging problem due to the unconstrained structure of the video content. Here, we propose a spatial pyramid based method for the recognition of video scenes and explore the effect of parameter optimization to the recognition accuracy. In the experiments different sampling methods, dictionary sizes, kernel methods, and pyramid levels are examined. Support Vector Machine (SVM) is employed for classification due to the success in pattern recognition applications. Our experiments show that, the size of dictionary and proper pyramid levels in feature representation drastically enhance the recognition accuracy.
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    Feature-level Fusion of Convolutional Neural Networks for Visual Object Classification
    (2016) Ergun, Hilal; Sert, Mustafa; https://orcid.org/0000-0002-7056-4245; AAB-8673-2019
    Deep learning architectures have shown great success in various computer vision applications. In this study, we investigate some of the very popular convolutional neural network (CNN) architectures, namely GoogleNet, AlexNet, VGG19 and ResNet. Furthermore, we show possible early feature fusion strategies for visual object classification tasks. Concatanation of features, average pooling and maximum pooling are among the investigated fusion strategies. We obtain state-of-the-art results on well-known image classification datasets of Caltech-101, Caltech-256 and Pascal VOC 2007.