Publication:
Small-Scale Deep Network for DCT-Based Images Classification

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dc.citedby6
dc.contributor.authorBorhanuddin B.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorChen S.D.en_US
dc.contributor.authorBaharuddin M.Z.en_US
dc.contributor.authorTan K.S.Z.en_US
dc.contributor.authorOoi T.W.M.en_US
dc.contributor.authorid57200577946en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid7410253413en_US
dc.contributor.authorid35329255600en_US
dc.contributor.authorid57216334027en_US
dc.contributor.authorid57193722449en_US
dc.date.accessioned2023-05-29T07:22:37Z
dc.date.available2023-05-29T07:22:37Z
dc.date.issued2019
dc.descriptionConvolutional neural networks; Deep neural networks; Digital storage; Discrete cosine transforms; Image classification; Image coding; Object recognition; Compressed images; Data redundancy; Discrete Cosine Transform(DCT); Images classification; Information reduction; Neural network model; Raw image data; Research trends; Image compressionen_US
dc.description.abstractThe need to acquire high performance deep neural network models is a research trend in recent years. Many examples have shown that achieving high validation accuracies require a very large number of parameters in most cases and therefore, the space used to store these models becomes very large. This may be a disadvantage on small storage size and low performance CPU edge devices during image processing that are embedded with neural networks for object recognition tasks. In this paper, we investigate the effect of input images which are partially compressed using the Discrete Cosine Transform (DCT) algorithm on two different Convolutional Neural Network (CNN) performances, known as CNN-C (large model) and CNN-RC3 (small model). DCT is used to reduce some data redundancies but also the risk of losing valuable features for the network to learn efficiently. However, the results show that both CNN architectures with DCT features perform as well as with raw image data, concluding that a properly designed CNN model can still achieve high performance on further compressed images regardless of its information reductions. � 2019 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9037777
dc.identifier.doi10.1109/ICRAIE47735.2019.9037777
dc.identifier.scopus2-s2.0-85083179653
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85083179653&doi=10.1109%2fICRAIE47735.2019.9037777&partnerID=40&md5=b7685dd07d0106c3f165c3ea29b11981
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24290
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleICRAIE 2019 - 4th International Conference and Workshops on Recent Advances and Innovations in Engineering: Thriving Technologies
dc.titleSmall-Scale Deep Network for DCT-Based Images Classificationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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