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	Selective Multi-Convolutional Region Feature Extraction based Iterative Discrimination CNN for Fine-Grained Vehicle Model Recognition
	Yanling Tian; Weitong Zhang; Qieshi Zhang; Gang Lu; Xiaojun Wu
	2018
会议日期	2018
英文摘要	Abstract—With the rapid rise of computer vision and driverless technology, vehicle model recognition plays a huge role in the common application and industry field. While fine-grained vehicle model recognition is often influenced by multi-level information, such as the image perspective, inter-feature similarity, vehicle details. Furthermore, pivotal regions extraction and finegrained feature learning have become a vital obstacle to the fine-grained recognition of vehicle models. In this paper, we propose an iterative discrimination CNN (ID-CNN) based on selective multi-convolutional region (SMCR) feature extraction. The SMCR features, which consist of global and local SMCR features, are extracted from the original image with higher activation response value. As for ID-CNN, we use the global and local SMCR features iteratively to localize deep pivotal features and concatenate them together into a fully-connected fusion layer to predict the vehicle categories. We get better results and improve the accuracy to 91:8% on Stanford Cars-196 data sets and to 96:2% on CompCars data sets. We have also tested the proposed method on two additional data sets VehicleModel-372 and VehicleModel-1326 and obtain higher accuracy than other state-of-the-art methods.
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/13779]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Yanling Tian,Weitong Zhang,Qieshi Zhang,et al. Selective Multi-Convolutional Region Feature Extraction based Iterative Discrimination CNN for Fine-Grained Vehicle Model Recognition[C]. 见:. 2018.

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