Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9023
Title: An Efficient Image Retrieval Model with Convolutional Neural Network based Text/Image Identification for Copyright Violation Detection
Other Titles: Telif Hakki Ihlali Tespiti için Evrişimsel Sinir A?i Tabanli Metin/Görüntü Tanimlamali Verimli Bir Görüntü Geri Getirme Modeli
Authors: Ozden H.
Tavli B.
Demirci M.F.
Keywords: Bag of Visual Words
Convolutional Neural Network
Image Retrieval
SIFT
Convolution
Convolutional neural networks
Neural network models
Bag-of-visual-words
Convolutional neural network
Image identification
Network-based
Retrieval models
SIFT
Test images
Text images
Third parties
Violation detections
Image retrieval
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: One of the most important problems faced by broadcasters is the unauthorized use of their images by third parties or organizations in a large-scale database, which contains hundreds of thousands of images. For this reason, it is important to perform an efficient and effective image retrieval, whose objective is to find the most similar images to a given test image. In addition, test images often contain text, and the presence of the text together with the visual part complicates the search process. In this paper, we present an image retrieval framework based on a bag of visual words, which has been shown to be effective in the literature. A convolutional neural network model is used to parse the text in the images. Experiments demonstrate the efficacy of this model in a large database. © 2022 IEEE.
Description: 30th Signal Processing and Communications Applications Conference, SIU 2022 -- 15 May 2022 through 18 May 2022 -- -- 182415
URI: https://doi.org/10.1109/SIU55565.2022.9864741
https://hdl.handle.net/20.500.11851/9023
ISBN: 9.78167E+12
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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