Abstract
We propose different methods for adaptively selecting information in images during object recognition. In contrast to standard feature selection, we consider this problem in a Bayesian framework where features are sequentially selected based on the current belief distribution over object classes. We define three different selection criteria and provide efficient Monte Carlo algorithms for the selection. In particular, we extend the successful Naive Bayes Nearest Neighbor (NBNN) classification approach, which is very costly to compute in its original form. We show that the proposed information selection methods result in a significant speed-up because only a small number of features needs to be extracted for accurate classification. In addition to adaptive methods based on the current belief distribution, we also consider image-based selection methods and we evaluate the performance of the different methods on a standard object recognition data set.
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References
Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, pp. 1–2 (2004)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding 106(1), 59–70 (2007); special issue on Generative Model Based Vision
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Krieger, G., Rentschler, I., Hauske, G., Schill, K., Zetzsche, C.: Object and scene analysis by saccadic eye-movements: An investigation with higher-order statistics. Spatial Vision 13(2–3), 201–214 (2000)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Liu, H., Sun, J., Liu, L., Zhang, H.: Feature selection with dynamic mutual information. Pattern Recognition 42(7), 1330–1339 (2009)
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer vision, 1999, vol. 2, pp. 1150–1157. IEEE (1999)
McCann, S., Lowe, D.G.: Local naive Bayes nearest neighbor for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3650–3656. IEEE (2012)
Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (2014)
Najemnik, J., Geisler, W.S.: Optimal eye movement strategies in visual search. Nature 434(7031), 387–391 (2005)
Kluth, T., Reineking, T., Nakath, D., Zetzsche, C., Schill, K.: Active sensorimotor object recognition in three-dimensional space. In: Freksa, C., Nebel, B., Hegarty, M., Barkowsky, T. (eds.) Spatial Cognition 2014. LNCS, vol. 8684, pp. 312–324. Springer, Heidelberg (2014)
Niedermeier, H., Clemens, J., Kowalski, J., Macht, S., Heinen, D., Hoffmann, R., Linder, P.: Navigation system for a research ice probe for antarctic glaciers. In: IEEE/ION PLANS 2014, pp. 959–975. IEEE (2014)
Pavone, M., Acikmese, B., Nesnas, I.A., Starek, J.: Spacecraft autonomy challenges for next generation space missions (2013), online (to appear in Lecture Notes in Control and Information Systems). http://goo.gl/nU8xG0
Reineking, T., Schill, K.: Evidential object recognition based on information gain maximization. In: Cuzzolin, F. (ed.) BELIEF 2014. LNCS, vol. 8764, pp. 227–236. Springer, Heidelberg (2014)
Schill, K., Umkehrer, E., Beinlich, S., Krieger, G., Zetzsche, C.: Scene analysis with saccadic eye movements: Top-down and bottom-up modeling. Journal of Electronic Imaging 10(1), 152–160 (2001)
Torralba, A., Oliva, A., Castelhano, M.S., Henderson, J.M.: Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychological Review 113(4), 766 (2006)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML, vol. 97, pp. 412–420 (1997)
Zetzsche, C., Barth, E.: Image surface predicates and the neural encoding of two-dimensional signal variations. In: SC-DL tentative, pp. 160–177. International Society for Optics and Photonics (1990)
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Reineking, T., Kluth, T., Nakath, D. (2015). Adaptive Information Selection in Images: Efficient Naive Bayes Nearest Neighbor Classification. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_29
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DOI: https://doi.org/10.1007/978-3-319-23192-1_29
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