Analyzing deep features for trademark retrieval Marka Erişimi İcin Derin Özniteliklerin İncelenmesi

Date

2017-05-18

Author

Aker, Cemal
Tursun, Osman
Kalkan, Sinan

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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

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Citation Formats

C. Aker, O. Tursun, and S. Kalkan, “Analyzing deep features for trademark retrieval Marka Erişimi İcin Derin Özniteliklerin İncelenmesi,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35921.