Abstract
Deep Metric learning (DML) is gaining popularity recently as a way of exploiting the advantages of deep learning in applications where the task involves adaption to variable object features, e.g. facial verification, person re-identification. In applications of deep learning where generalisability is difficult to achieve, DML provides an architecture which has the facility for the algorithm’s output’s to be adapted to each use case by framing classification tasks as a reidentification problem. At the inference stage, query embeddings generated by the DML model are compared against a gallery of embeddings in a latent space. This paper will investigate online database management strategies to preserve the quality and diversity of data and the representation of each class in the gallery of embeddings. We propose the use of SQL to facilitate the removal of outliers from an embedding database and also discuss latent works which study the geometric and statistical relationships between embeddings to formulate methods for outlier embeddings removal.
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References
Sanyal, S.: Discriminative Descriptors for Unconstrained Face and Object Recognition (2017)
Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification (2017)
Zheng, L., Yang, Y., Hauptmann, A.G.: Person Re-identification: Past, Present and Future (2016)
Wang, H., Li, H., Peng, J., Fu, X.: Multi-feature distance metric learning for non-rigid 3D shape retrieval. Multimedia Tools Appl. 78(21), 30943–30958 (2019). https://doi.org/10.1007/s11042-019-7670-9
Boiarov, A., Tyantov, E.: Large Scale Landmark Recogni-tion via Deep Metric Learning (2019). https://doi.org/10.1145/3357384.3357956
Bonadiman, D., Kumar, A., Mittal, A.: Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning (2019)
da Silva, A.C.M., Coelho, M.A.N., Neto, R.F.: A Music Classification model based on metric learning applied to MP3 audio files. Expert Syst. Appl. 144,(2020). https://doi.org/10.1016/j.eswa.2019.113071
Thakur, A., Thapar, D., Rajan, P., Nigam, A.: Deep metric learning for bioacoustic classification: overcoming training data scarcity using dynamic triplet loss. J. Acoust. Soc. Am. 146, 534–547 (2019). https://doi.org/10.1121/1.5118245
Marasović, T., Papić, V.: Accelerometer based gesture recognition system using distance metric learning for nearest neighbour classification. In: IEEE International Workshop on Machine Learning for Signal Processing, MLSP (2012)
Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018, 1–13 (2018). https://doi.org/10.1155/2018/7068349
Chen-Yi, L., Rustia, D.J.A., Lin, T.-T.: Generative adversarial network based image augmentation for insect pest classification enhancement. IFAC-PapersOnLine 52(30), 1–5 (2019). https://doi.org/10.1016/j.ifacol.2019.12.406
O’ Mahony, N., Campbell, S., Carvalho, A., et al.: One-shot learning for custom identification tasks: a review. In: 29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2019). Elsevier B.V., Limerick (2019)
Li, W., Huo, J., Shi, Y., et al.: Online Deep Metric Learning (2018)
Lin, X., Duan, Y., Dong, Q., et al.: Deep Variational Metric Learning. In: Computer Vision and Pattern Recognition (CVPR) (201AD)
Liu, M., Vemuri, B.C.: A robust and efficient doubly regularized metric learning approach. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV, pp. 646–659. Springer Berlin Heidelberg, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_46
Parameswaran, S., Weinberger, K.Q.: Large margin multi-task metric learning. In: NIPS’10 Proceedings of the 23rd International Conference on Neural Information Processing Systems (2010)
Huang, C., Loy, C.C., Tang, X.: Local Similarity-Aware Deep Feature Embedding (2016)
O’Mahony, N., Campbell, S., Carvalho, A., et al.: Point Cloud Annotation Methods for 3D Deep Learning. Institute of Electrical and Electronics Engineers (IEEE), pp 1–6 (2020)
Fu, J., Rui, Y.: Advances in deep learning approaches for image tagging. APSIPA Trans. Signal Inf. Process. 6,(2017). https://doi.org/10.1017/ATSIP.2017.12
Xu, H., Caramanis, C., Mannor, S.: Outlier-robust PCA: the high-dimensional case. IEEE Trans. Inf. Theory 59, 546–572 (2013). https://doi.org/10.1109/TIT.2012.2212415
Cerioli, A.: Multivariate outlier detection with high-breakdown estimators. J. Am. Stat. Assoc. 105, 147–156 (2010). https://doi.org/10.1198/jasa.2009.tm09147
Gong, X., Shen, L., Lu, T.: Refining training samples using median absolute deviation for supervised classification of remote sensing images. J. Indian Soc. Remote Sens. 47(4), 647–659 (2018). https://doi.org/10.1007/s12524-018-0887-7
Wang, Z., Wang, Y.: Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders. BMC Bioinf. 20, 568 (2019). https://doi.org/10.1186/s12859-019-3130-9
Reunanen, N., Räty, T., Jokinen, J.J., Hoyt, T., Culler, D.: Unsupervised online detection and prediction of outliers in streams of sensor data. Int. J. Data Sci. Analyt. 9(3), 285–314 (2019). https://doi.org/10.1007/s41060-019-00191-3
Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood Components Analysis (2005)
Hermans, A., Beyer, L., Leibe, B.: In Defense of the Triplet Loss for Person Re-Identification (2017)
Mahbub, U., Sarkar, S., Patel, V.M., Chellappa, R.: Active user authentication for smartphones: a challenge data set and benchmark results. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS 2016 (2016). https://doi.org/10.1109/BTAS.2016.7791155
Acien, A., Morales, A., Vera-Rodriguez, R., et al.: Multi lock: mobile active authentication based on multiple biometric and behavioral patterns. MULEA 2019 - 1st International Workshop on Multimodal Understanding and Learning for Embodied Applications, co-located with MM 2019, pp. 53–59 (2019). https://doi.org/10.1145/3347450.3357663
Abavisani, M., Patel, V.M.: Deep sparse representation-based classification. IEEE Signal Process. Lett. 26, 948–952 (2019). https://doi.org/10.1109/LSP.2019.2913022
Nienkötter, A., Jiang, X.: A lower bound for generalized median based consensus learning using kernel-induced distance functions. Pattern Recogn. Lett. 140, 339–347 (2020). https://doi.org/10.1016/j.patrec.2020.11.003
Lin, W., Wang, Y., Gao, J., Li, X.: Deep adaptive feature embedding with local sample distributions for person re-identification. Pattern Recogn. 73, 275–288 (2018). https://doi.org/10.1016/j.patcog.2017.08.029
Lin, X., Duan, Y., Dong, Q., Jiwen, L., Zhou, J.: Deep variational metric learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 714–729. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_42
Acknowledgments
This work was supported, in part, by Science Foundation Ireland grant 13/RC/2094 and co-funded under the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero - the Irish Software Research Centre (www.lero.ie). The authors wish to acknowledge the DJEI/DES/SFI/HEA Irish Centre for High-End Computing (ICHEC) for the provision of computational facilities and support.
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Mahony, N.O., Campbell, S., Carvalho, A., Krpalkova, L., Riordan, D., Walsh, J. (2022). Improving Accuracy and Latency in Image Re-identification by Gallery Database Cleansing. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_60
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DOI: https://doi.org/10.1007/978-3-030-80119-9_60
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