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AuthorBelhi, Abdelhak
AuthorBouras, Abdelaziz
Available date2023-04-09T08:34:47Z
Publication Date2020
Publication Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
ResourceScopus
URIhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089643
URIhttp://hdl.handle.net/10576/41716
AbstractModern applications for cultural content enrichment and management require low delay image retrieval methods in large databases. Classical image retrieval methods are suitable for certain applications but are also known to lack the ability of generalization and their use for low delay applications for retrieval tasks is not investigated for the context of cultural heritage. As a potential improvement, we propose a new approach for large scale image retrieval that uses a pre-trained CNN as a global features extractor and a clustering model trained on these features to regroup similarly looking images. For retrieval, this model quickly identifies the closest cluster and then, the matching is only carried out for the images of the selected cluster. As a result, our approach does not require indexation and the preliminary results show that it is suitable for real-time and low delay applications as in the matching step, no heavy processing is required. Some of the suitable applications include quick image search and digital rights management. 2020 IEEE.
SponsorACKNOWLEDGEMENT This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors (www.ceproqha.qa).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDeep learning
Digital heritage
Image classification
Image clustering
Image retrieval
TitleCNN Features vs Classical Features for Largescale Cultural Image Retrieval
TypeConference Paper
Pagination95-99
dc.accessType Abstract Only


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