Abstract
Automatic image annotation is an important and challenging task when managing large image collections. In this paper, we present an incremental approach for shape labeling, which is useful to image annotation when new sets of images are available during time. Every time new shape images are available, a semi-supervised fuzzy clustering algorithm is used to group shapes into a number of clusters by exploiting knowledge about classes expressed as a set of pre-labeled shapes. Each cluster is represented by a prototype that is manually labeled and used to annotate shapes. To capture the evolution of the image set, the previously discovered prototypes are added as pre-labeled shapes to the current shape set before clustering. The performance of the proposed incremental approach is evaluated on an image dataset from the fish domain, which is divided into chunks of data to simulate the progressive availability of shapes during time.
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References
Abbasi, S., Mokhtarian, F., Kittler, J.: SQUID Demo Dataset 1,500 (1997), http://www.ee.surrey.ac.uk/Research/VSSP/imagedb/demo.html
Bartolini, I., Ciaccia, P., Patella, M.: WARP: Accurate retrieval of shapes using phase of Fourier descriptors and Time warping distance. IEEE Transaction on Pattern Analysis and Machine Intelligence 27(1), 142–147 (2005)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)
Castellano, G., Fanelli, A.M., Torsello, M.A.: A fuzzy set approach for shape-based image annotation. In: Fanelli, A.M., Pedrycz, W., Petrosino, A. (eds.) WILF 2011. LNCS (LNAI), vol. 6857, pp. 236–243. Springer, Heidelberg (2011)
Castellano, G., Fanelli, A.M., Torsello, M.A.: Fuzzy image labeling by partially supervised shape clustering. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part II. LNCS, vol. 6882, pp. 84–93. Springer, Heidelberg (2011)
Chapelle, O., Schoelkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)
Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams: Theory and practice. IEEE Trans. on Knowledge and Data Engineering 15(3), 515–528 (2003)
Hore, P., Hall, L., Goldgof, D., Cheng, W.: Online fuzzy c means. In: Fuzzy Information Processing Society, NAFIPS 2008, pp. 1–5 (2008)
Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: 2006 SIAM Conference on Data Mining, pp. 328–339 (2006)
Yixin, C., Tu, L.: Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2007)
Lew, M., Sebe, N., Djeraba, C., Lifl, F., Ramesh, J.: Content-based Multimedia Information Retrieval: State of the Art and Challenges. ACM Transactions on Multimedia Computing, Communications, and Applications, 1–19 (2006)
Pedrycz, W., Waletzky, J.: Fuzzy clustering with partial supervision. IEEE Transaction System Man Cybernetics 27(5), 787–795 (1997)
Ruiz, C., Menasalvas, E., Spiliopoulou, M.: C-denStream: Using domain knowledge on a data stream. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 287–301. Springer, Heidelberg (2009)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Analisys and Machine Intelligence 22(12), 1349–1380 (2000)
Veltkamp, R., Tanase, M.: Content-based image retrieval systems: a survey. Technical Report (2001)
Wu, J., Xiong, H., Chen, J.: Adapting the right measures for k-means clustering. In: Proceedings of the KDD 2009 Conference, pp. 877–885. ACM (2009)
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Castellano, G., Fanelli, A.M., Torsello, M.A. (2013). Shape Annotation by Incremental Semi-supervised Fuzzy Clustering. In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_20
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DOI: https://doi.org/10.1007/978-3-319-03200-9_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03199-6
Online ISBN: 978-3-319-03200-9
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