Abniki, Somayeh2022-04-202022-04-2020222022-04-19http://hdl.handle.net/1828/13861The 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.enAvailable to the World Wide Webentropylocal relative entropycolor recognitionDNNprojectAbniki, 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_46