Abstract
Visual descriptors are widely used in several recognition and classification tasks in robotics. The main challenge for these tasks is to find a descriptor that could represent the image content without losing representative information of the image. Nowadays, there exists a wide range of visual descriptors computed with computer vision techniques and different pooling strategies. This paper proposes a novel way for building image descriptors using an external tool, namely: Clarifai. This is a remote web tool that allows to automatically describe an input image using semantic tags, and these tags are used to generate our descriptor. The descriptor generation procedure has been tested in the ViDRILO dataset, where it has been compared and merged with some well-known descriptors. Moreover, subset variable selection techniques have been evaluated. The experimental results show that our descriptor is competitive in classification tasks with the results obtained with other kind of descriptors.
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References
Banerji, S., Sinha, A., Liu, C.: Novel color, shape and texture-based scene image descriptors. In: 2012 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 245–248, August 2012
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 271–2727 (2011)
Clarifai: Clarifai: Amplifying Intelligence (2015). http://www.clarifai.com/
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Int. Conf. on CVPR, vol. 1, pp. 886–893. IEEE (2005)
Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, GCE 2008, pp. 1–10. Ieee (2008)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012)
Lampert, C., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(3), 453–465 (2014)
Li, L.J., Fei-Fei, L.: What, where and who? Classifying events by scene and object recognition. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)
Li, L.J., Su, H., Lim, Y., Fei-Fei, L.: Objects as attributes for scene classification. In: Kutulakos, K. (ed.) Trends and Topics in Computer Vision. Lecture Notes in Computer Science, vol. 6553, pp. 57–69. Springer, Heidelberg (2012)
Martinez-Gomez, J., Cazorla, M., Garcia-Varea, I., Morell, V.: Vidrilo: The visual and depth robot indoor localization with objects information dataset. International Journal of Robotics Research (2015)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)
Petty, K.F., Moylan, A.J., Kwon, J., Mewes, J.J.: Traffic state estimation with integration of traffic, weather, incident, pavement condition, and roadway operations data (February 5, 2014), uS Patent App. 14/173,611
Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: IEEE International Workshop on Proceedings of the Content-Based Access of Image and Video Database, pp. 42–51. IEEE (1998)
Wang, C., Blei, D., Li, F.F.: Simultaneous image classification and annotation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1903–1910, June 2009
Wang, G., Hoiem, D., Forsyth, D.: Learning image similarity from flickr groups using fast kernel machines. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(11), 2177–2188 (2012)
Winder, S., Brown, M.: Learning local image descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8, June 2007
Wohlkinger, W., Vincze, M.: Ensemble of shape functions for 3D object classification. In: 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2987–2992. IEEE (2011)
Wu, J., Rehg, J.M.: Centrist: A visual descriptor for scene categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(8), 1489–1501 (2011)
Zhou, X., Yu, K., Zhang, T., Huang, T.: Image classification using super-vector coding of local image descriptors. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision ECCV 2010. Lecture Notes in Computer Science, vol. 6315, pp. 141–154. Springer, Heidelberg (2010)
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Rangel, J.C., Cazorla, M., García-Varea, I., Martínez-Gómez, J., Fromont, É., Sebban, M. (2016). Computing Image Descriptors from Annotations Acquired from External Tools. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-319-27149-1_52
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DOI: https://doi.org/10.1007/978-3-319-27149-1_52
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