Abstract
This paper introduces a novel approach for querying samples to be labeled in active learning for image recognition. The user is able to efficiently label images with a visualization for training a classifier. This visualization is achieved by using dimension reduction techniques to create a 2D feature embedding from high-dimensional features. This is made possible by a querying strategy specifically designed for the visualization, seeking optimized bounding-box views for subsequent labeling. The approach is implemented in a web-based prototype. It is compared in-depth to other active learning querying strategies within a user study we conducted with 31 participants on a challenging data set. While using our approach, the participants could train a more accurate classifier than with the other approaches. Additionally, we demonstrate that due to the visualization, the number of labeled samples increases and also the label quality improves.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
Cavallo, M., Demiralp, Ç.: A visual interaction framework for dimensionality reduction based data exploration. In: Conference on Human Factors in Computing Systems CHI, p. 635 (2018)
Hasler, S., Kreger, J., Bauer-Wersing, U.: Interactive incremental online learning of objects onboard of a cooperative autonomous mobile robot. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11307, pp. 279–290. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04239-4_25
Hinton, G.E., Roweis, S.T.: Stochastic neighbor embedding. In: Advances in Neural Information Processing Systems (NIPS), pp. 857–864 (2003)
Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24(6), 417 (1933)
Huang, L., Matwin, S., de Carvalho, E.J., Minghim, R.: Active learning with visualization for text data. In: ACM Workshop on Exploratory Search and Interactive Data Analytics, pp. 69–74 (2017)
Iwata, T., Houlsby, N., Ghahramani, Z.: Active learning for interactive visualization. In: International Conference on Artificial Intelligence and Statistics AISTATS, pp. 342–350 (2013)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Croft, B.W., van Rijsbergen, C.J. (eds.) SIGIR 1994, pp. 3–12. Springer, London (1994). https://doi.org/10.1007/978-1-4471-2099-5_1
Liao, H., Chen, L., Song, Y., Ming, H.: Visualization-based active learning for video annotation. IEEE Trans. Multimedia 18(11), 2196–2205 (2016)
Limberg, C., Wersing, H., Ritter, H.: Efficient accuracy estimation for instance-based incremental active learning. In: European Symposium on Artificial Neural Networks (ESANN), pp. 171–176 (2018)
Limberg, C., Wersing, H., Ritter, H.: Improving active learning by avoiding ambiguous samples. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 518–527. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01418-6_51
Losing, V., Hammer, B., Wersing, H.: Interactive online learning for obstacle classification on a mobile robot. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2015)
Maaten, L.v.d., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Ramirez-Loaiza, M.E., Sharma, M., Kumar, G., Bilgic, M.: Active learning: an empirical study of common baselines. Data Min. Knowl. Disc. 31(2), 287–313 (2017)
Settles, B.: Active learning. Synth. Lect. Artif. Intell. Mach. Learn. 6(1), 1–114 (2012)
Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1070–1079 (2008)
Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Conference on Computational Learning Theory (COLT), pp. 287–294 (1992)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Torgerson, W.S.: Multidimensional scaling: I. Theory and method. Psychometrika 17(4), 401–419 (1952)
Zhang, D., Zhou, Z., Chen, S.: Semi-supervised dimensionality reduction. In: SIAM International Conference on Data Mining, pp. 629–634 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Limberg, C., Krieger, K., Wersing, H., Ritter, H. (2019). Active Learning for Image Recognition Using a Visualization-Based User Interface. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-30484-3_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30483-6
Online ISBN: 978-3-030-30484-3
eBook Packages: Computer ScienceComputer Science (R0)