Pairwise Similarity for Line Extraction from Distorted Images
Clustering a given set of data is crucial in many fields including image processing. It plays important roles in image segmentation and object detection for example. This paper proposes a framework of building a similarity matrix for a given dataset, which is then used for clustering the dataset. The similarity between two points are defined based on how other points distribute around the line connecting the two points. It can capture the degree of how the two points are placed on the same line. The similarity matrix is considered as a kernel matrix of the given dataset, and based on it, the spectral clustering is performed. Clustering with the proposed similarity matrix is shown to perform well through experiments using an artificially designed problem and a real-world problem of detecting lines from a distorted image.
Keywordspairwise similarity spectral clustering line detection distorted image
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- 1.Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall (1988)Google Scholar
- 2.Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: NIPS, pp. 849–856 (2001)Google Scholar
- 4.Avidan, S., Butman, M.: The power of feature clustering: An application to object detection. In: NIPS, pp. 57–64 (2004)Google Scholar
- 9.Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: KDD, pp. 551–556 (2004)Google Scholar
- 12.Pelleg, D., Moore, A.W.: X-means: Extending k-means with efficient estimation of the number of clusters. In: ICML, pp. 727–734 (2000)Google Scholar
- 13.Kalogeratos, A., Likas, A.: Dip-means: an incremental clustering method for estimating the number of clusters. In: NIPS, pp. 2402–2410 (2012)Google Scholar