Abstract
In this paper we consider the problem of image content recognition and we address it by using local features and kNN based classification strategies. Specifically, we define a number of image similarity functions relying on local features comparing their performance when used with a kNN classifier. Furthermore, we compare the whole image similarity approach with a novel two steps kNN based classification strategy that first assigns a label to each local feature in the document to be classified and then uses this information to assign a label to the whole image. We perform our experiments solving the task of recognizing landmarks in photos.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Amato, G., Falchi, F.: Local feature based image similarity functions for kNN classfication. In: Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART 2011), vol. 1, pp. 157–166. SciTe Press (2011)
Amato, G., Falchi, F., Bolettieri, P.: Recognizing landmarks using automated classification techniques: an evaluation of various visual features. In: Proceeding of the Second Interantional Conference on Advances in Multimedia (MMEDIA 2010), Athens, Greece, June 13-19, pp. 78–83. IEEE Computer Society (2010)
Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)
Batko, M., Novak, D., Falchi, F., Zezula, P.: Scalability comparison of peer-to-peer similarity search structures. Future Generation Comp. Syst. 24(8), 834–848 (2008)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: CVPR (2008)
Chen, T., Wu, K., Yap, K.H., Li, Z., Tsai, F.S.: A survey on mobile landmark recognition for information retrieval. In: MDM 2009: Proc. of the Tenth International Conference on Mobile Data Management, pp. 625–630. IEEE (2009)
Dudani, S.: The distance-weighted k-nearest-neighbour rule. IEEE Transactions on Systems, Man and Cybernetics SMC-6(4), 325–327 (1975)
Fagni, T., Falchi, F., Sebastiani, F.: Image classification via adaptive ensembles of descriptor-specific classifiers. Pattern Recognition and Image Analysis 20, 21–28 (2010), http://dx.doi.org/10.1134/S1054661810010025
Falchi, F.: Pisa landmarks dataset (2011), http://www.fabriziofalchi.it/pisaDataset/ (last accessed on March 3, 2011)
Google: Google Goggles (2011), http://www.google.com/mobile/goggles/ (last accessed on March 3, 2011)
Jégou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. Int. J. Comput. Vision 87(3), 316–336 (2010)
Kennedy, L.S., Naaman, M.: Generating diverse and representative image search results for landmarks. In: WWW 2008: Proceeding of the 17th International Conference on World Wide Web, pp. 297–306. ACM Press, New York (2008)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Samet, H.: Foundations of Multidimensional and Metric Data Structures. Computer Graphics and Geometric Modeling. Morgan Kaufmann Publishers Inc., San Francisco (2005)
Serdyukov, P., Murdock, V., van Zwol, R.: Placing flickr photos on a map. In: Allan, J., Aslam, J.A., Sanderson, M., Zhai, C., Zobel, J. (eds.) SIGIR, pp. 484–491. ACM (2009)
Yeh, T., Tollmar, K., Darrell, T.: Searching the web with mobile images for location recognition. In: CVPR (2), pp. 76–81 (2004)
Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. In: Advances in Database Systems, vol. 32. Springer, Heidelberg (2006)
Zheng, Y., Song, M.Z., Adam, Y., Buddemeier, H., Bissacco, U., Brucher, A., Chua, F., Neven, T.S., Tour, H.: The world: Building a web-scale landmark recognition engine. In: CVPR, pp. 1085–1092. IEEE (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Amato, G., Falchi, F. (2013). On kNN Classification and Local Feature Based Similarity Functions. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2011. Communications in Computer and Information Science, vol 271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29966-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-29966-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29965-0
Online ISBN: 978-3-642-29966-7
eBook Packages: Computer ScienceComputer Science (R0)