Abstract
In intelligent warehouse logistics, image classification is widely used. However, the performance of traditional image classifiers suffers when deployed in new environments because of the lack of labels collected. In this paper, aiming at solving the problem above, a novel method named non-negative discriminative collective target nearest-neighbour representation (NDCTNNR) is proposed. Inspired by the Collective target nearest-neighbour representation (CTNNR), this method introduces a novel regularization term to integrate class discrimination and data locality. Moreover, our method uses non-negative representations to make collaborative representation sparser. The experimental results confirm the effectiveness of the proposed method.
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References
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. Comput. Vision Image Underst. 110(01), 404–417 (2006)
Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7103–7112 (2018)
Flamary, R., Courty, N., Tuia, D., Rakotomamonjy A.: Optimal transport for domain adaptation. IEEE Trans. Pattern Anal. Mach, Intell (2016)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE pp. 2066–2073 (2012)
Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: An unsupervised approach. In: Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, 11 (2011), pp. 999–1006
Han, W., Feng, R., Wang, L., Gao, L.: Adaptive spatial-scale-aware deep convolutional neural network for high-resolution remote sensing imagery scene classification. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE 4736–4739 (2018)
Jadoon, W., Yi, Z., Zhang, L.: Collaborative neighbor representation based classification using l2-minimization approach. Pattern Recogn. Lett. 34(1), 201–2018 (2013)
Kumar, A., Das, S.D.: Bird species classification using transfer learning with multistage training. In: Arora, C., Mitra, K. (eds.) Computer Vision Applications, pp. 28–38. Springer Singapore, Singapore (2019)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, 12 (2013), pp. 2200–2207
Mi, J.-X., Sun, Y., Lu, J., Kong, H.: Robust supervised sparse representation for face recognition. Cogn. Syst. Res. 62, 10–22 (2020)
Milhomem, S., Almeida, T.D., da Silva, W.G., de Carvalho, R.: Weightless neural network with transfer learning to detect distress in asphalt. Int. J. Adv. Eng. Res. Sci 5(12), 294–299 (2018)
Pan S.,Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. vol. 22, pp. 1345 – 1359, 11 (2010)
Pan, S., Tsang, I., Kwok, J., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22, 199–210 (2011)
Raja, R., Roomi, S.M.M., Dharmalakshmi D.: Robust indoor/outdoor scene classification. In: 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR). IEEE, pp. 1–5 (2015)
Tang, S., Chen, Z., Chen, L., Ye, M.: Domain adaptation of image classification exploiting target adaptive collaborative local-neighbor representation. In: 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, pp. 154–157 (2016a)
Tang, S., Ye, M., Liu, Q.: Domain adaptation of image classification based on collective target nearest-neighbor representation. J. Electr. Imaging 25(3), 033006 (2016b)
Tao, D., Geng, B.: Bregman divergence-based regularization for transfer subspace learning. IEEE Trans. Knowl. Data Eng 22(7), 929–942 (2010)
Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: The IEEE International conference on data mining (ICDM), 2017, pp. 1129–1134
Xu, Y., Zhong, Z., Yang, J., You, J., Zhang, D.: A new discriminative sparse representation method for robust face recognition via l2 regularization. IEEE Trans. Neural Netw. Learn. Syst 28(10), 2233–2242 (2017)
Zhang, L.: Transfer adaptation learning: A decade survey, (2019)
Zhang, F., Zhu, X., Ye, M. Fast human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3517–3526 (2019)
Zhu, Y., Zhuang, F., Yang, J., Yang, X., He, Q.: Adaptively transfer category-classifier for handwritten chinese character recognition. In: Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 110–122. Springer International Publishing, Cham (2019)
Acknowledgements
This work is supported by the Natural Science Foundation of China (Nos. 61672299, 61972208, 61602259, 61701251, 61803213 and 61972211), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Nos. 18KJB520035, 18KJB510016) and National Engineering Laboratory for Logistics Information Technology, YuanTong Express Co. LTD.
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Sun, Y., Zhou, J., Sun, Y. et al. Non-negative discriminative collective target nearest-neighbor representation. Int J Intell Robot Appl 6, 1–9 (2022). https://doi.org/10.1007/s41315-021-00169-0
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DOI: https://doi.org/10.1007/s41315-021-00169-0