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Computer Science > Computer Vision and Pattern Recognition

arXiv:1911.07440 (cs)
[Submitted on 18 Nov 2019 (v1), last revised 12 Mar 2022 (this version, v4)]

Title:Large Scale Open-Set Deep Logo Detection

Authors:Muhammet Bastan, Hao-Yu Wu, Tian Cao, Bhargava Kota, Mehmet Tek
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Abstract:We present an open-set logo detection (OSLD) system, which can detect (localize and recognize) any number of unseen logo classes without re-training; it only requires a small set of canonical logo images for each logo class. We achieve this using a two-stage approach: (1) Generic logo detection to detect candidate logo regions in an image. (2) Logo matching for matching the detected logo regions to a set of canonical logo images to recognize them.
We constructed an open-set logo detection dataset with 12.1k logo classes and released it for research this http URL demonstrate the effectiveness of OSLD on our dataset and on the standard Flickr-32 logo dataset, outperforming the state-of-the-art open-set and closed-set logo detection methods by a large margin. OSLD is scalable to millions of logo classes.
Comments: Open Set Logo Detection (OSLD) dataset available at this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1911.07440 [cs.CV]
  (or arXiv:1911.07440v4 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1911.07440

Submission history

From: Muhammet Bastan [view email]
[v1] Mon, 18 Nov 2019 05:44:17 UTC (1,574 KB)
[v2] Sun, 29 Aug 2021 23:04:01 UTC (1,372 KB)
[v3] Sun, 16 Jan 2022 07:05:05 UTC (1,372 KB)
[v4] Sat, 12 Mar 2022 23:47:45 UTC (1,458 KB)
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