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Title: Logo detection using weakly supervised saliency map
Authors: Kumar G.
Keserwani P.
Pratim Roy, Partha
Dogra D.P.
Published in: Multimedia Tools and Applications
Abstract: Box level annotation of a large number of logo images for training purpose of typical deep learning architecture is highly challenging. Thus, a method that can detect the logo with the help of training to remove box-level annotations can be helpful. In this paper, we present a method of logo detection that utilizes weakly supervised learning of Convolutional Neural Network (CNN) to generate a deep saliency map. The saliency map is generated from the back-propagated response of the CNN trained with the classification task. The saliency map produces responses for the regions of logos. GrabCut segmentation method has been applied then to obtain the bounding box corresponding to the logo class predicted by the CNN for a given image. AlexNet, CaffeNet, and VGGNet deep architectures has been fine-tuned for the classification purpose. The framework is further utilized for detection through a back-propagated saliency map. The performance of the proposed methodology has been validated on the FlickrLogos-32 logo benchmark dataset. The proposed method outperforms the state-of-the-art baseline fully supervised methods with mean average precision (mAP) of 75.83%. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Citation: Multimedia Tools and Applications, 80(3): 4341-4365
Issue Date: 2021
Publisher: Springer
Keywords: Convolutional neural network
Logo detection
Saliency map
Weakly supervised
ISSN: 13807501
Author Scopus IDs: 57213515807
Author Affiliations: Kumar, G., Department of CSE, Indian Institute of Technology Roorkee, Roorkee, India
Keserwani, P., Department of CSE, Indian Institute of Technology Roorkee, Roorkee, India
Roy, P.P., Department of CSE, Indian Institute of Technology Roorkee, Roorkee, India
Dogra, D.P., School of Electrical Sciences, IIT Bhubaneswar, Bhubaneswar, India
Funding Details: The authors would like to acknowledge the support of DST-SERB. The Project ID is SB/S3/EECE/099/2016. SB/S3/EECE/099/2016
Corresponding Author: Kumar, G.; Department of CSE, India; email:
Appears in Collections:Journal Publications [CS]

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