TY - GEN
T1 - Machine Learning-Based Low-Complexity Image Feature Descriptor for MPEG-CDVS Standard
AU - Jiang, You Wei
AU - Chen, You Rong
AU - Lin, Shih Hsiang
AU - Chen, Pei Yin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Compact descriptors for visual search (CDVS) is a completed standard from the ISO/IEC moving pictures experts group (MPEG). CDVS has a low complexity and bitrate efficiency on image matching and retrieval. The MPEG CDVS framework detects key feature points in the image and extracts descriptors to obtain bitstreams to match images. We proposed a low-complexity image descriptor for visual searching within the MPEG CDVS framework. The proposed algorithm integrated traditional image processing methods with a machince learning (ML) network, resulting in reduced compression time and the size of the CDVS standard bitstream. To evaluate the effectiveness of the proposed algorithm, we tested the proposed algorithm with the CDVS standard software to ensure its compatibility. On the CDVS-Benchmark, the experimental results demonstrated that the proposed algorithm reduced matching time by 31.7%, bitstream size by 31.8%, and extracting time by 12.8% compared to the CDVS standard test model. Similarly, on the HPatches-Benchmark, the proposed algorithm reduced matching time by 5.3%, bitstream size by 25.2%, and extracting time by 32.0% compared to the CDVS standard test model.
AB - Compact descriptors for visual search (CDVS) is a completed standard from the ISO/IEC moving pictures experts group (MPEG). CDVS has a low complexity and bitrate efficiency on image matching and retrieval. The MPEG CDVS framework detects key feature points in the image and extracts descriptors to obtain bitstreams to match images. We proposed a low-complexity image descriptor for visual searching within the MPEG CDVS framework. The proposed algorithm integrated traditional image processing methods with a machince learning (ML) network, resulting in reduced compression time and the size of the CDVS standard bitstream. To evaluate the effectiveness of the proposed algorithm, we tested the proposed algorithm with the CDVS standard software to ensure its compatibility. On the CDVS-Benchmark, the experimental results demonstrated that the proposed algorithm reduced matching time by 31.7%, bitstream size by 31.8%, and extracting time by 12.8% compared to the CDVS standard test model. Similarly, on the HPatches-Benchmark, the proposed algorithm reduced matching time by 5.3%, bitstream size by 25.2%, and extracting time by 32.0% compared to the CDVS standard test model.
UR - http://www.scopus.com/inward/record.url?scp=85180809692&partnerID=8YFLogxK
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U2 - 10.1109/ICKII58656.2023.10332711
DO - 10.1109/ICKII58656.2023.10332711
M3 - Conference contribution
AN - SCOPUS:85180809692
T3 - Proceedings of the 2023 IEEE 6th International Conference on Knowledge Innovation and Invention, ICKII 2023
SP - 464
EP - 469
BT - Proceedings of the 2023 IEEE 6th International Conference on Knowledge Innovation and Invention, ICKII 2023
A2 - Meen, Teen-Hang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023
Y2 - 11 August 2023 through 13 August 2023
ER -