Sezen, Arda

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Profile URL
https://hdl.handle.net/20.500.14411/6766
Name Variants
A., Sezen
A.,Sezen
Sezen,A.
S.,Arda
S., Arda
Arda Sezen
Sezen,Arda
Sezen, Arda
Arda, Sezen
Job Title
Yardımcı Doçent
Email Address
arda.sezen@atilim.edu.tr
Main Affiliation
Computer Engineering
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Website
ORCID ID
0000-0002-7615-3623
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Turkish CoHE Profile ID
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WoS Researcher ID

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Scholarly Output

11

Articles

6

Views / Downloads

58/532

Supervised MSc Theses

4

Supervised PhD Theses

1

WoS Citation Count

7

Scopus Citation Count

36

Patents

0

Projects

1

WoS Citations per Publication

0.64

Scopus Citations per Publication

3.27

Open Access Source

5

Supervised Theses

5

JournalCount
International Journal of Computational and Experimental Science and Engineering3
Bilişim Teknolojileri Dergisi1
Evolutionary Intelligence1
IEEE Access1
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    Article
    Citation - WoS: 2
    Citation - Scopus: 3
    A Hybrid Approach for Semantic Image Annotation
    (Ieee-inst Electrical Electronics Engineers inc, 2021) Sezen, Arda; Turhan, Cigdem; Sengul, Gokhan
    In this study, a framework that generates natural language descriptions of images within a controlled environment is proposed. Previous work on neural networks mostly focused on choosing the right labels and/or increasing the number of related labels to depict an image. However, creating a textual description of an image is a completely different phenomenon, structurally, syntactically, and semantically. The proposed semantic image annotation framework presents a novel combination of deep learning models and aligned annotation results derived from the instances of the ontology classes to generate sentential descriptions of images. Our hybrid approach benefits from the unique combination of deep learning and semantic web technologies. We detect objects from unlabeled sports images using a deep learning model based on a residual network and a feature pyramid network, with the focal loss technique to obtain predictions with high probability. The proposed framework not only produces probabilistically labeled images, but also the contextual results obtained from a knowledge base exploiting the relationship between the objects. The framework's object detection and prediction performances are tested with two datasets where the first one includes individual instances of images containing everyday scenes of common objects and the second custom dataset contains sports images collected from the web. Moreover, a sample image set is created to obtain annotation result data by applying all framework layers. Experimental results show that the framework is effective in this controlled environment and can be used with other applications via web services within the supported sports domain.