Abstract
Once images are decomposed into a number of visual primitives, it is of great interests to cluster these primitives into mid-level visual patterns. However, conventional clustering of visual primitives, e.g., bag-of-words, usually ignores the spatial context and multi-feature information among the visual primitives and thus cannot discover mid-level visual patterns of complex structure. To overcome this problem, we propose to consider both spatial and feature contexts among visual primitives for visual pattern discovery in this chapter. We formulate the pattern discovery task as a multi-context-aware clustering problem and propose a self-learning procedure to iteratively refine the result until it converges. By discovering both spatial co-occurrence patterns among visual primitives and feature co-occurrence patterns among different types of features, the proposed method can better address the ambiguities of visual primitives.
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Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the International Conference on Image and Video Retrieval, pp. 401–408 (2007)
Lee, Y., Grauman, K.: Object-graphs for context-aware visual category discovery. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 346–358 (2012)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Malisiewicz, T., Efros, A.: Improving apatial support for objects via multiple segmentations. In: Proceedings of British Machine Vision Conference, vol. 2 (2007)
Russell, B., Freeman, W., Efros, A., Sivic, J., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1605–1614 (2006)
Su, Y., Jurie, F.: Visual word disambiguation by semantic contexts. In: Proceedings of IEEE International Conference on Computer Vision, pp. 311–318 (2011)
Tuytelaars, T., Lampert, C., Blaschko, M., Buntine, W.: Unsupervised object discovery: a comparison. Int. J. Comput. Vis. 88(2), 284–302 (2010)
Wang, H., Yuan, J., Wu, Y.: Context-aware discovery of visual co-occurrence patterns. IEEE Trans. Image Process. 23(4), 1805–1819 (2014)
Weng, C., Wang, H., Yuan, J.: Hierarchical sparse coding based on spatial pooling and multi-feature fusion. In: Proceedings of the IEEE International Conference on Multimedia Expo, pp. 1–6 (2013)
Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: Proceedings of IEEE International Conference on Computer Vision (2005)
Yuan, J., Wu, Y.: Context-aware clustering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Yuan, J., Wu, Y.: Mining visual collocation patterns via self-supervised subspace learning. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 1–13 (2012)
Yuan, J., Wu, Y., Yang, M.: From frequent itemsets to semantically meaningful visual patterns. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 864–873 (2007)
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Wang, H., Weng, C., Yuan, J. (2017). Context-Aware Discovery of Visual Co-occurrence Patterns. In: Visual Pattern Discovery and Recognition. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-4840-1_2
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DOI: https://doi.org/10.1007/978-981-10-4840-1_2
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