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Proceedings Paper

A vehicle logo recognition method based on improved SIFT feature and bag-of-words model
Author(s): Zhiliang Huang; Xiaobo Lu; Cong Chen
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Paper Abstract

Vehicle logo, as the key information of vehicle, combined with other vehicle characteristics will make vehicle management more effective in the intelligent transportation system. However, it is still a challenging task to extract effective features for vehicle logo recognition, largely due to its variations in illumination and low resolution. Aiming at improving the recognition rate of vehicle logo recognition, this paper proposes a new vehicle logo recognition method. First, in the aspect of vehicle logo feature extraction, a vehicle logo feature extraction algorithm based on the fusion of SIFT features and Dense-SIFT features was put forward to generate local feature descriptors. Then Bag-of-words model was used to describe vehicle logo features and form visual dictionary histogram. Considering that bag-of-words model ignores spatial structure information of objects, we introduced spatial pyramid model into bag-of-words model. In the aspect of vehicle logo recognition, vehicle logo was classified by using Support Vector Machine (SVM) based on one-against-the-rest multiclassification structure. Finally, our method was verified effectively through the experiment compared to other methods.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061Y (9 August 2018); doi: 10.1117/12.2502972
Show Author Affiliations
Zhiliang Huang, Southeast Univ. (China)
Xiaobo Lu, Southeast Univ. (China)
Cong Chen, Southeast Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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