Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Author: search.filters.author.Özuysal, MustafaSubject: search.filters.subject.Image classification

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  • Master Thesis
    Hierarchical Image Classification With Self-Supervised Vision Transformer Features
    (Izmir Institute of Technology, 2022) Karagüler, Caner; Özuysal, Mustafa
    There are lots of works about image classification and most of them are based on convolutional neural networks (CNN). In image classification, some classes are more difficult to distinguish than others because of non-even visual separability. These difficult classes require domain-specific classifiers but traditional convolutional neural networks are trained as flat N-way classifiers. These flat classifiers can not leverage the hierarchical information of the classes well. To solve this issue, researchers proposed new techniques that embeds class-hierarchy into the convolutional neural networks and most of these techniques exceed existing convolutional neural networks' success rates on large-scale datasets like ImageNet. In this work, we questioned if a hierarchical image classification with self- supervised vision transformer features can exceed hierarchical convolutional neural networks. During this work, we used a hierarchical ETHEC dataset and extract attention features with the help of vision transformers. Using these attention features, we implemented 3 different hierarchical classification approaches and compared the results with CNN alternative of our approaches.
  • Master Thesis
    Keypoint Detection and Description on Image Curves
    (Izmir Institute of Technology, 2017) Köksal, Ali; Özuysal, Mustafa
    Image curves are one of the choices for representing interest points which also provide discriminative information about images. Boundary of regions and contour of shapes are real-time instances of image curves. In this thesis, we propose two approaches for keypoint detection and description on image curves. To extract keypoints on image curves, we compute the extrema curvature of region boundaries. This mechanism improves repeatability of keypoints on 3D data. For the description of image curves, shape contours are used. This is similar to approaches that describe the features based on shapes and image gradients. Unlike these approaches, we combine spatial and directional information of tangent directions to extract a feature vector that leads to improved matching and recognition on several standard computer vision tasks such as character and object recognition.