Abstract
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.
The proposed joint CNN-based model was implemented and tested on the Stanford Cars dataset and the CompCars dataset. Our experimental results are compared with those of other methods, and verify the superior performance of the proposed model.
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References
Wen, X., Shao, L., Xue, Y., Fang, W.: A rapid learning algorithm for vehicle classification. Inf. Sci. 295(1), 395–406 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS), vol. 1, pp. 1097–1105. Curran Associates, Inc. (2012)
Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 834–849. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10590-1_54
Krause, J., Jin, H., Yang, J., Fei-Fei, L.: Fine-grained recognition without part annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 5546–5555. IEEE (2015)
Lin, T.-Y., RoyChowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 1449–1457. IEEE (2015)
Sudowe, P., Spitzer, H., Leibe, B.: Person attribute recognition with a jointly-trained holistic CNN model. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), vol. 1, pp. 87–95. IEEE (2015)
Lin, D., Shen, X., Cewu, L., Jia, J.: Deep LAC: deep localization, alignment and classification for fine-grained recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 1666–1674. IEEE (2015)
Simon, M., Rodner, E.: Neural activation constellations: unsupervised part model discovery with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 1143–1151. IEEE (2015)
Xie, S., Wang, Y.: Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wireless Pers. Commun. 78(1), 231–246 (2014)
Shen, J., Tan, H., Wang, J., Wang, J., Lee, S.: A novel routing protocol providing good transmission reliability in underwater sensor networks. J. Internet Technol. 16(1), 171–178 (2015)
Guo, P., Wang, J., Li, B., Lee, S.: A variable threshold-value authentication architecture for wireless mesh networks. J. Internet Technol. 15(6), 929–936 (2014)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia (ACM MM), vol. 1, pp. 675–678. ACM (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 1–9. IEEE (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 580–587. IEEE (2014)
Chen, Q., Huang, J., Feris, R., Brown, L.M., Dong, J., Yan, S.: Deep domain adaptation for describing people based on fine-grained clothing attributes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 5315–5324. IEEE (2015)
Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vis. (IJCV) 104(2), 154–171 (2013)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NIPS), vol. 1, pp. 91–99. Curran Associates, Inc. (2015)
Xie, S., Yang, T., Wang, X., Lin, Y.: Hyper-class augmented and regularized deep learning for fine-grained image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 2645–2654. IEEE (2015)
Zhang, L., Yang, Y., Wang, M., Hong, R., Nie, L., Li, X.: Detecting densely distributed graph patterns for fine-grained image categorization. IEEE Trans. Image Process. 25(2), 553–565 (2016)
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), vol. 1, pp. 554–561. IEEE (2013)
Yang, L., Luo, P., Loy, C.C., Tang, X.: A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 3973–3981. IEEE (2015)
Acknowledgments
This work was supported in part by the Natural Science Foundation of China under Grant U1536203 and 61272409, in part by the Major Scientific and Technological Innovation Project of Hubei Province under Grant 2015AAA013.
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Liu, M., Yu, C., Ling, H., Lei, J. (2016). Hierarchical Joint CNN-Based Models for Fine-Grained Cars Recognition. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_30
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