Abstract
The use of local features has demonstrated its effectiveness for many visual applications. However, local features are often extracted with gray images. This ignores the useful information within different color channels which eventually hinders the final performance, especially for fine-grained image classification. Besides, the semantic information of local features is too weak to be applied for high-level visual applications. To cope with these problems, in this paper, we propose a novel fine-grained image classification method by using color exemplar classifiers. For each image, we first decompose it into multiple color channels to take advantage of the color information. For each color channel, we represent each image with a response histogram which is generated by exemplar classifiers. Experiments on several public image datasets demonstrate the effectiveness of the proposed color exemplar classifier based image classification method.
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Zhang, C., Xiong, W., Liu, J., Zhang, Y., Liang, C., Huang, Q. (2013). Fine-Grained Image Classification Using Color Exemplar Classifiers. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_31
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DOI: https://doi.org/10.1007/978-3-319-03731-8_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03730-1
Online ISBN: 978-3-319-03731-8
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