Identifying Vehicle Exterior Color by Image Processing and Deep Learning

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dc.contributor.authorAbniki, Somayeh
dc.contributor.supervisorFun Li, Kin
dc.date.accessioned2022-04-20T00:37:26Z
dc.date.available2022-04-20T00:37:26Z
dc.date.copyright2022en_US
dc.date.issued2022-04-19
dc.degree.departmentDepartment of Electrical and Computer Engineeringen_US
dc.degree.levelMaster of Engineering M.Eng.en_US
dc.description.abstractThe vehicle’s color is one of the factors considered in car purchasing. Hence, color extraction and identification from online vehicle images play an important role in the vehicle e-commerce marketplace. In this paper, we present a vehicle color identification methodology. Image processing techniques are employed to construct feature vectors, which are then used as input to deep neural networks to classify a vehicle’s color into 14 classes. Local relative entropy is utilized as a measure of image segmentation to select the region of interest. Experiments are performed on an image dataset provided by an automobile ecommerce operator. Our implementation results are evaluated and discussed.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationAbniki, S., Li, K.F., Avant, T. (2022). Identifying Vehicle Exterior Color by Image Processing and Deep Learning. In: Barolli, L., Kulla, E., Ikeda, M. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-030-95903-6_46en_US
dc.identifier.urihttp://hdl.handle.net/1828/13861
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectentropyen_US
dc.subjectlocal relative entropyen_US
dc.subjectcolor recognitionen_US
dc.subjectDNNen_US
dc.titleIdentifying Vehicle Exterior Color by Image Processing and Deep Learningen_US
dc.typeprojecten_US

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