BigyaPAn: Deep Analysis of Old Paper Advertisement

Adak, C; Tao, X

BigyaPAn: Deep Analysis of Old Paper Advertisement

Adak, C

Tao, X

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 International Joint Conference on Neural Networks (IJCNN), 2021, 2021-July
Issue Date:: 2021-09-20

Closed Access

	Filename	Description	Size
	BigyaPAn_Deep_Analysis_of_Old_Paper_Advertisement.pdf	Published version	15.3 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Adak, C https://orcid.org/0000-0002-9085-2770
dc.contributor.author	Tao, X
dc.date	2021-07-18
dc.date.accessioned	2022-06-27T01:35:36Z
dc.date.available	2022-06-27T01:35:36Z
dc.date.issued	2021-09-20
dc.identifier.citation	2021 International Joint Conference on Neural Networks (IJCNN), 2021, 2021-July
dc.identifier.isbn	9780738133669
dc.identifier.issn	2161-4393
dc.identifier.uri	http://hdl.handle.net/10453/158385
dc.description.abstract	In this paper, we work on analyzing old paper advertisement (Ad). An Ad usually contains various types of textual and non-textual objects, which may also be in different orientations. We attempt to detect such objects from an early Indian-print paper Ad database comprising 1500 Ad images. The past major object detectors did not perform well on this database. We propose a deep reinforcement learning-based orientation-aware object detector. Our system learns by itself where to look and what to look of an Ad image. Therefore, it can bypass the impeding zone due to degraded image quality. To find the looking spot, we come up with a foveal transformation. In reinforcement learning, we present a scheme for shaping an internal reward with a top-up. For oriented object detection, we also propose a generic loss function. Our system obtained encouraging results from the experiments performed on the Ad database.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartof	2021 International Joint Conference on Neural Networks
dc.relation.ispartofseries	IEEE International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/ijcnn52387.2021.9533299
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	BigyaPAn: Deep Analysis of Old Paper Advertisement
dc.type	Conference Proceeding
utslib.citation.volume	2021-July
utslib.location.activity	Shenzhen, China
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-27T01:35:28Z
pubs.finish-date	2021-07-22
pubs.publication-status	Published
pubs.start-date	2021-07-18
pubs.volume	2021-July

Abstract:

In this paper, we work on analyzing old paper advertisement (Ad). An Ad usually contains various types of textual and non-textual objects, which may also be in different orientations. We attempt to detect such objects from an early Indian-print paper Ad database comprising 1500 Ad images. The past major object detectors did not perform well on this database. We propose a deep reinforcement learning-based orientation-aware object detector. Our system learns by itself where to look and what to look of an Ad image. Therefore, it can bypass the impeding zone due to degraded image quality. To find the looking spot, we come up with a foveal transformation. In reinforcement learning, we present a scheme for shaping an internal reward with a top-up. For oriented object detection, we also propose a generic loss function. Our system obtained encouraging results from the experiments performed on the Ad database.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/158385