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.
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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)
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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
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DOI: https://doi.org/10.1007/978-3-642-29966-7_15
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