Abstract： The Convolution Neural Network(CNN) in Deep Learning has a strong anti-jamming ability for image translation, rota-
tion and other transformations. Compared with the traditional vehicle recognition technology, it can extract deeper and richer image
information. Based on the VGGNet structure and simulating the order of human eyes' perception of vehicle characteristics, this pa-
per designs a hierarchical retrieval system for vehicle image database. Firstly, a CNN which can recognize eight kinds of colors is
constructed and trained to recognize the color of the target vehicle. Then, SIFT and LBP features are combined to match and retrieve
the same color candidate vehicle database. The hierarchical retrieval mode of the system can effectively reduce the scope of retrieval
and improve the efficiency of retrieval. The fusion of multi features can also guarantee the extraction of enough image information
and ensure the accuracy of retrieval.
顾思思 扈乐华. 基于VGGNet颜色识别的车辆检索系统设计[J]. 电脑与电信, .
GU Si-si HU Le-hua. Design of Vehicle Retrieval System Based on VGGNet Color Recognition. Computer & Telecommunication, 2020, 1(7): 17-20.
