Abstract
The algorithm selection has been successfully used on a variety of decision problems. When the problem definition is structured and several algorithms for the same problem are available, then meta-features, that in turn permit a highly accurate algorithm selection on a case-by-case basis, can be easily and at a relatively low cost extracted. Real world problems such as computer vision could benefit from algorithm selection as well, however the input is not structured and datasets are very large both in samples size and sample numbers. Therefore, meta-features are either impossible or too costly to be extracted. Considering such limitations, in this paper we experimentally evaluate the cost and the complexity of algorithm selection on two popular computer vision datasets VOC2012 and MSCOCO and by using a variety task oriented features. We evaluate both dataset on algorithm selection accuracy over five algorithms and by using a various levels of dataset manipulation such as data augmentation, algorithm selector fine tuning and ensemble selection. We determine that the main reason for low accuracy from existing features is due to insufficient evaluation of existing algorithms. Our experiments show that even without meta features, it is thus possible to have meaningful algorithm selection accuracy, and thus obtain processing accuracy increase. The main result shows that using ensemble method, trained on MSCOCO dataset, we can successfully increase the processing result by at least 3% of processing accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aguiar, G.J., Mantovani, R.G., Mastelini, S.M., de Carvalho, A.C., Campos,G.F., Junior, S.B.: A meta-learning approach for selecting image segmentation algorithm. Pattern Recogn. Lett. 128, 480–487 (2019). https://doi.org/10.1016/j.patrec.2019.10.018, http://www.sciencedirect.com/science/article/pii/S0167865519302983
Ali, S., Smith, K.: On learning algorithm selection for classification. Appl. Soft Comput. 6, 119–138 (2006)
Bischl, B., et al.:ASlib: a benchmark library for algorithm selection. Artif. Intell. 237, 41–58 (2016). https://doi.org/10.1016/j.artint.2016.04.003, http://www.sciencedirect.com/science/article/pii/S0004370216300388
Bosch, M., Gifford, C., Dress, A., Lau, C., Skibo, J.: Improved image segmentation via cost minimization of multiple hypotheses. In: T.-K., Kim, Zafeiriou, G.B.S., Mikolajczyk, K. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 7.1–7.12. BMVA Press, September 2017. https://doi.org/10.5244/C.31.7
Carreira, J., Li, F., Sminchisescu, C.: Object recognition by sequential figure-ground ranking. Int. J. Comput. Vis. 98(3), 243–262 (2012)
Chawla, N., Bower, K.W., Hall, L., Kegelmayer, W.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. CoRR abs/1412.7062 (2014). http://arxiv.org/abs/1412.7062
Chinchor, N.: MUC-4 evaluation metrics. In: Proceedings of the 4th Conference on Message Understanding. MUC4 1992, pp. 22–29. Association for Computational Linguistics, USA (1992). https://doi.org/10.3115/1072064.1072067
Cruz, R.M.O., Sabourin, R., Cavalcanti, G.D.C.: Dynamic classifier selection: recent advances and perspectives. Inf. Fusion 41, 195–216 (2018)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Gunawan, A., Lau, H.C., Misir, M.: Designing and comparing multiple portfolios of parameter configurations for online algorithm selection. In: Festa, P., Sellmann, M., Vanschoren, J. (eds.) Learning and Intelligent Optimization - 10th International Conference, LION 10, 29 May – 1 June 2016, Ischia, Italy, Revised Selected Papers. Lecture Notes in Computer Science, vol. 10079, pp. 91–106. Springer (2016). https://doi.org/10.1007/978-3-319-50349-3_7
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: European Conference on Computer Vision, pp. 297–312 (2014). https://doi.org/10.1007/978-3-319-10584-0_20
Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin del la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)
Kerschke, P., Hoos, H.H., Neumann, F., Trautmann, H.: Automated algorithm selection: survey and perspectives. Evol. Comput. 27(1), 3–45 (2019). https://doi.org/10.1162/evco_a_00242, pMID: 30475672
Kim, Y., Jang, T., Han, B., Choi, S.: Learning to select pre-trained deep representations with Bayesian evidence framework. CoRR abs/1506.02565 (2015). http://arxiv.org/abs/1506.02565
Ladicky, L., Russell, C., Kohli, P., Torr, P.: Graph cut based inference with co-occurrence statistics. In: Proceedings of the 11th European Conference on Computer Vision, pp. 239–253 (2010). https://doi.org/10.1007/978-3-642-15555-0_18
Leyton-Brown, K., Nudelman, E., Andrew, G., Mcfadden, J., Shoham, Y.: A portfolio approach to algorithm selection. In: IJCAI, vol. 3, pp. 1542–1543 (2003)
Lin, T., et al.: Microsoft COCO: common objects in context. CoRR abs/1405.0312 (2014). http://arxiv.org/abs/1405.0312
Lindauer, M., Hoos, H., Hutter, F.: From sequential algorithm selection to parallel portfolio selection. In: LION (2015)
Lindauer, M., Hoos, H.H., Hutter, F., Schaub, T.: AutoFolio: an automatically configured algorithm selector. J. Artif. Int. Res. 53(1), 745–778 (2015)
Lukac, M., Abdiyeva, K., Kim, A., Kameyama, M.: Reasoning and algorithm selection augmented symbolic segmentation. In: Intelligent Systems Conference (2017)
van Maaren, H., Franco, J.: The International SAT Competition Web Page (2002). http://satcompetition.org/
Mnih, V., et al.: Playing atari with deep reinforcement learning. CoRR abs/1312.5602 (2013). http://arxiv.org/abs/1312.5602
Murdock, C., Li, Z., Zhou, H., Duerig, T.: Blockout: dynamic model selection for hierarchical deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Muñoz, M.A., Sun, Y., Kirley, M., Halgamuge, S.K.: Algorithm selection forblack-box continuous optimization problems: a survey on methods and challenges. Inf. Sci. 317, 224 – 245 (2015). https://doi.org/10.1016/j.ins.2015.05.010, http://www.sciencedirect.com/science/article/pii/S0020025515003680
Rice, J.: The algorithm selection problem. Adv. Comput. 15, 65118 (1976)
Rusu, A.A., et al.: Progressive neural networks. CoRR abs/1606.04671 (2016). http://arxiv.org/abs/1606.04671
Wang, J.X., et al.: Learning to reinforcement learn. CoRR abs/1611.05763 (2016). http://arxiv.org/abs/1611.05763
Wang, Z., de Freitas, N., Lanctot, M.: Dueling network architectures for deep reinforcement learning. CoRR abs/1511.06581 (2015). http://arxiv.org/abs/1511.06581
Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. 32, 565–606, July 2008. https://doi.org/10.1613/jair.2490
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. CoRR abs/1611.01578 (2016). http://arxiv.org/abs/1611.01578
Acknowledgment
This work was funded by the FCDRGP research grant entitled LFC: Intention Estimation: A Live Feeling Approach from Nazarbayev University with reference number 240919FD3936.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lukac, M. et al. (2021). Selecting Algorithms Without Meta-features. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-68799-1_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68798-4
Online ISBN: 978-3-030-68799-1
eBook Packages: Computer ScienceComputer Science (R0)