Self-similarity matrix

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In data analysis, the self-similarity matrix is a graphical representation of similar sequences in a data series.

Contents

Similarity can be explained by different measures, like spatial distance (distance matrix), correlation, or comparison of local histograms or spectral properties (e.g. IXEGRAM [1] ). A similarity plot can be the starting point for dot plots or recurrence plots.

Definition

To construct a self-similarity matrix, one first transforms a data series into an ordered sequence of feature vectors , where each vector describes the relevant features of a data series in a given local interval. Then the self-similarity matrix is formed by computing the similarity of pairs of feature vectors

where is a function measuring the similarity of the two vectors, for instance, the inner product . Then similar segments of feature vectors will show up as path of high similarity along diagonals of the matrix. [2] Similarity plots are used for action recognition that is invariant to point of view [3] and for audio segmentation using spectral clustering of the self-similarity matrix. [4]

Example

Similarity plot, a variant of recurrence plot, obtained for different views of human actions are shown to produce similar patterns. Junejoetal ssm eccv08.jpg
Similarity plot, a variant of recurrence plot, obtained for different views of human actions are shown to produce similar patterns.

See also

References

  1. M. A. Casey; A. Westner (July 2000). "Separation of mixed audio sources by independent subspace analysis" (PDF). Proc. Int. Comput. Music Conf. Retrieved 2013-11-19.
  2. Müller, Meinard; Michael Clausen (2007). "Transposition-invariant self-similarity matrices" (PDF). Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007): 47–50. Retrieved 2013-11-19.
  3. I.N. Junejo; E. Dexter; I. Laptev; Patrick Pérez (2008). "Cross-View Action Recognition from Temporal Self-similarities". Computer Vision – ECCV 2008. Lecture Notes in Computer Science. Vol. 5303. pp. 293–306. CiteSeerX   10.1.1.405.1518 . doi:10.1007/978-3-540-88688-4_22. ISBN   978-3-540-88685-3.
  4. Dubnov, Shlomo; Ted Apel (2004). "Audio segmentation by singular value clustering". Proceedings of Computer Music Conference (ICMC 2004). CiteSeerX   10.1.1.324.4298 .
  5. Cross-View Action Recognition from Temporal Self-Similarities (2008), I. Junejo, E. Dexter, I. Laptev, and Patrick Pérez)

Further reading