Abstract
The development of Convolutional Neural Networks (CNNs) has resulted in significant improvements to object classification and detection in image data. One of their primary benefits is that they learn image features rather than relying on hand-crafted features, thereby reducing the amount of knowledge engineering that must be performed. However, another form of knowledge engineering bias exists in how objects are labelled in images, thereby limiting CNNs to classifying the set of object types that have been predefined by a domain expert. We describe a case-based method for detecting novel object types using a combination of an image’s raw pixel values and detectable parts. Our approach works alongside existing CNN architectures, thereby leveraging the state-of-the-art performance of CNNs, and is able to detect novel classes using limited training instances. We evaluate our approach using an existing object detection dataset and provide evidence of our approach’s ability to classify images even if the object in the image has not been previously encountered.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This example assumes a step size of 1, where the center on the filter is moved by 1 pixel at each step. However, in practice the step size can be set as a parameter.
References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 26th Annual Conference on Neural Information Processing Systems, pp. 1106–1114, Lake Tahoe, USA (2012)
Girshick, R.B., 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, pp. 580–587. IEEE Computer Society, Columbus (2014)
Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1979–1986. IEEE Computer Society, Columbus (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE Computer Society, Las Vegas (2016)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Sani, S., Wiratunga, N., Massie, S.: Learning deep features for kNN-based Human Activity Recognition. In: Proceedings of the International Conference on Case-Based Reasoning Workshops, pp. 95–103. CEUR Workshop Proceedings, Trondheim (2017)
Sani, S., Wiratunga, N., Massie, S., Cooper, K.: kNN sampling for personalised human activity recognition. In: Aha, D.W., Lieber, J. (eds.) ICCBR 2017. LNCS (LNAI), vol. 10339, pp. 330–344. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61030-6_23
Martin, K., Wiratunga, N., Sani, S., Massie, S., Clos, J.: A Convolutional siamese network for developing similarity knowledge in the SelfBACK dataset. In: Proceedings of the International Conference on Case-Based Reasoning Workshops, pp. 85–94. CEUR Workshop Proceedings, Trondheim (2017)
Grace, K., Maher, M.L., Wilson, D.C., Najjar, N.A.: Combining CBR and deep learning to generate surprising recipe designs. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS (LNAI), vol. 9969, pp. 154–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47096-2_11
Perner, P., Holt, A., Richter, M.: Image processing in case-based reasoning. Knowledge Engineering Review 20(3), 311–314 (2005)
Macura, R.T., Macura, K.J.: MacRad: Radiology image resource with a case-based retrieval system. In: Veloso, M., Aamodt, A. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 43–54. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60598-3_5
Haddad, M., Adlassnig, K.-P., Porenta, G.: Feasibility analysis of a case-based reasoning system for automated detection of coronary heart disease from myocardial scintigrams. Artif. Intell. Med. 9(1), 61–78 (1997)
Allampalli-Nagaraj, G., Bichindaritz, I.: Automatic semantic indexing of medical images using a web ontology language for case-based image retrieval. Eng. Appl. Artif. Intell. 22(1), 18–25 (2009)
Perner, P., Bühring, A.: Case-based object recognition. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 375–388. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28631-8_28
Micarelli, A., Neri, A., Sansonetti, G.: A case-based approach to image recognition. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS, vol. 1898, pp. 443–454. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44527-7_38
López-Sánchez, D., Corchado, J.M., González Arrieta, A.: A CBR system for efficient face recognition under partial occlusion. In: Aha, D.W., Lieber, J. (eds.) ICCBR 2017. LNCS (LNAI), vol. 10339, pp. 170–184. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61030-6_12
López-Sánchez, D., Corchado, J.M., González Arrieta, A.: A CBR system for image-based webpage classification: case representation with convolutional neural networks. In: Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, pp. 483–488. AAAI Press, Marco Island (2017)
Tuytelaars, T., Lampert, C.H., Blaschko, M.B., Buntine, W.L.: Unsupervised object discovery: a comparison. Int. J. Comput. Vis. 88(2), 284–302 (2010)
Zhu, J.-Y., Wu, J., Xu, Y., Chang, E., Tu, Z.: Unsupervised object class discovery via saliency-guided multiple class learning. IEEE Trans. Pattern Anal. Mach. Intell. 37(4), 862–875 (2015)
Chen, X., Shrivastava, A., Gupta, A.: Enriching visual knowledge bases via object discovery and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2035–2042. IEEE Computer Society, Columbus (2014)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Hartigan, J.A.: Clustering Algorithms. Wiley, New York (1975)
Aaron, B., Tamir, D.E., Rishe, N.D., and Kandel, A.: Dynamic incremental k-means clustering. In: Proceedings of the International Conference on Computational Science and Computational Intelligence, pp. 308–313. IEEE Press, Las Vegas (2014)
Acknowledgements
Thanks to the Office of Naval Research for supporting this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Turner, J.T., Floyd, M.W., Gupta, K.M., Aha, D.W. (2018). Novel Object Discovery Using Case-Based Reasoning and Convolutional Neural Networks. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-01081-2_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01080-5
Online ISBN: 978-3-030-01081-2
eBook Packages: Computer ScienceComputer Science (R0)