Abstract
We propose the proximity clustering (PxC) algorithm, which finds the spatially diverse but semantically similar data-point colonies in the form of a dominant class cluster. It searches through different proximity metric spaces for revealing the hidden porous data pattern. For each data-point, it provides the degree of belongingness to a dominant cluster and thereby performing the soft clustering. Performance evaluation on an artificial dataset, for finding a dominant class, shows that the PxC outperforms the other clustering methods. We experimentally validate its applications for image classification, segmentation and an abnormal event detection from the video using the proposed motion features. Experimental results show that the PxC improves performance of the image classification and the unsupervised image segmentation.
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References
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: An effcient data clustering method for very large databases. In: ACM SIGMOD Conf. on Management of Data (1996)
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data. In: Int. Conf. on KDDM (2005)
Guha, S., Rastogi, R., Shim, K.: Cure: An efcient clustering algorithm for large databases. In: Proc of ACM SIGMOD (1998)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. on Neural Networks (2000)
Ng, R., Han, J.: Efficient and effective clustering methods for spatial data mining. In: Proc. of the VLDB Conference (1994)
Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. Jrnl. of Royal Stat. Society-Series (2001)
Sandhan, T., Srivastava, T., Sethi, A., Choi, J.: Unsupervised learning approach for abnormal event detection in surveillance video by revealing infrequent patterns. In: IEEE Int. Conf. on Image and Vision Computing, New Zealand, IVCNZ (2013)
Sandhan, T., Choi, J.: Frequencygrams and multi-feature joint sparse representation for action and gesture recognition. In: IEEE ICIP (2014)
Ester, M., Kriegel, H., Jorg, S., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. AAAI Press (1996)
Shi, J., Malik, J.: Normalized cuts and image segmentation. In: PAMI (2000)
Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: NIPS (2001)
Brendan, J., Delbert, D.: Clustering by passing messages between data points. Science (2007)
Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. PAMI (2007)
Galluccio, L., Michel, O., Comon, P., Kliger, M., Hero, A.: Clustering with a new distance measure based on a dual-rooted tree. Information Sciences (2013)
Wei-jiang, X., Liu-sheng, H., Yong-long, L., Yi-fei, Y., Wei-wei, J.: Protocols for privacy-preserving dbscan clustering. Int. J. of Security and Its App. (2007)
Sandhan, T., Yoo, Y., Yoo, H., Yun, S., Byeon, M.: Multi-task learning with over-sampled time-series representation of a trajectory for traffic motion pattern recognition. In: IEEE Int. Conf. on Adv. Video & Signal-Based Surv., AVSS (2014)
Sandhan, T., Sonowal, S., Choi, J.: Audio bank: A high-level acoustic signal representation for audio event recognition. In: IEEE ICCAS (2014)
Sandhan, T., Chang, H., Choi, J.: Abstracted radon profiles for fingerprint recognition. In: IEEE Int. Conf. on Image Processing, ICIP (2013)
UMN data (2010), http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi
Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)
Caltech (2011), http://www.vision.caltech.edu/ImageDatasets/Caltech6
Sandhan, T., Choi, J.: Handling imbalanced datasets by partially guided hybrid sampling for pattern recognition. In: IEEE ICPR (2014)
Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: ICCV (2005)
Deng, H., Clausi, D.: Unsupervised image segmentation using a simple mrf model with a new implementation scheme. In: Pattern Recognition (2004)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: CVPR (2009)
Saligrama, V., Chen, Z.: Video anomaly detection based on local statistical aggregates. In: CVPR (2012)
Wu, S., Moore, B., Shah, M.: Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: CVPR (2010)
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Sandhan, T., Yun, K., Choi, J.Y. (2014). Proximity Clustering for Revealing a Semantically Dominant Class. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_7
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DOI: https://doi.org/10.1007/978-3-319-14364-4_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
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