Hierarchical Joint CNN-Based Models for Fine-Grained Cars Recognition

For the purpose of public security, car detection and identification are urgently required in the real time traffic monitoring system. However, fine-grained recognition is a challenging task in the area of computer vision due to the subtle inter-class and huge intra-class differences. To tackle this task, this paper provided a novel approach focussed on two main aspects. On the one hand, the most discriminative local feature representations of regions of interests (ROIs) magnified many details. On the other hand, the hierarchical relations within the fine-grained categories can be simulated by probability formulas. Our proposed model consists of two modules: (i) a region proposal network to generate plenty of ROIs and (ii) a joint CNN-based model to learn the multi-grained feature representations simultaneously.

[1]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Jian Shen,et al.  A Novel Routing Protocol Providing Good Transmission Reliability in Underwater Sensor Networks , 2015 .

[3]  Jian Dong,et al.  Deep domain adaptation for describing people based on fine-grained clothing attributes , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Xuelong Li,et al.  Detecting Densely Distributed Graph Patterns for Fine-Grained Image Categorization , 2016, IEEE Transactions on Image Processing.

[5]  Ling Shao,et al.  A rapid learning algorithm for vehicle classification , 2015, Inf. Sci..

[6]  Trevor Darrell,et al.  Part-Based R-CNNs for Fine-Grained Category Detection , 2014, ECCV.

[7]  Cewu Lu,et al.  Deep LAC: Deep localization, alignment and classification for fine-grained recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[10]  Xiaoou Tang,et al.  A large-scale car dataset for fine-grained categorization and verification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Jonathan Krause,et al.  Fine-grained recognition without part annotations , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Jonathan Krause,et al.  3D Object Representations for Fine-Grained Categorization , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[13]  Marcel Simon,et al.  Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[15]  Subhransu Maji,et al.  Bilinear CNN Models for Fine-Grained Visual Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[16]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Tianbao Yang,et al.  Hyper-class augmented and regularized deep learning for fine-grained image classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Bastian Leibe,et al.  Person Attribute Recognition with a Jointly-Trained Holistic CNN Model , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[19]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Yuxiang Wang,et al.  Construction of Tree Network with Limited Delivery Latency in Homogeneous Wireless Sensor Networks , 2014, Wirel. Pers. Commun..

[21]  Jin Wang,et al.  A Variable Threshold-Value Authentication Architecture for Wireless Mesh Networks , 2014 .