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Analyzing deep features for trademark retrieval Marka Erişimi İcin Derin Özniteliklerin İncelenmesi
Date
2017-05-18
Author
Aker, Cemal
Tursun, Osman
Kalkan, Sinan
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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The rapid rise in the amount of trademark applications and trademark infringements has led the trademark retrieval (TR) to become an important and formidable task to solve. Existing studies based on hand-crafted features show unsatisfying performance. Taking the popularization and increasing success of the deep learning methods into consideration, in this work, many well-known Convolutional Neural Network (CNN) models are applied to the TR problem. Models are tested using a large scale trademark dataset in contrast with the previously proposed solutions, and their failure points are discussed in this study. For the problems that can be encountered, solutions such as tine-tuning, distance metric learning, using CNN features locally, and making them invariant to aspect ratio of the trademark are suggested.
Subject Keywords
Deep learning
,
Trademark retrieval
URI
https://hdl.handle.net/11511/35921
DOI
https://doi.org/10.1109/siu.2017.7960426
Collections
Department of Computer Engineering, Conference / Seminar