Whence and what are thou, execrable shape?
John Milton (Paradise Lost bk. II, l. 681)
This webpage contains links to our papers on shape mining and indexing.
1)
Eamonn Keogh, Li Wei, Xiaopeng Xi, SangHee Lee and Michail Vlachos
(2006)
LB_Keogh Supports Exact
Indexing of Shapes under Rotation Invariance with Arbitrary
Representations and Distance Measures.
VLDB 2006.
2) Li Wei, Eamonn Keogh and Xiaopeng Xi (2006) SAXually Explicit Images: Finding Unusual Shapes. ICDM 2006. [pdf][Powerpoint][ expanded journal version]. 3) Dragomir Yankov and Eamonn Keogh. (2006). Manifold Clustering of Shapes ICDM 2006. [pdf] 4) Ken Ueno, Xiaopeng Xi, Eamonn Keogh, DahJye Lee (2006). Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining. ICDM 2006. [pdf] (Note: This paper is not exclusively about shape, but does show how to classify shapes with any anytime algorithm). 5) Xiaopeng Xi, Eamonn Keogh, Li Wei, Agenor MafraNeto (2007). (tentative title) Finding Motifs in Database of Shapes. SIAM International Conference on Data Mining (SDM2007). 6) Xiaoyue Wang, Lexiang Ye, Eamonn Keogh and Christian Shelton. (2008). Annotating Historical Archives of Images. JCDL 2008 [pdf] 7) Lexiang Ye, Xiaoyue Wang, Dragomir Yankov and Eamonn Keogh (2008). The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining. SDM 2008. [pdf] Datasets are here. (Note: This paper is not exclusively about shape, but one experiment does deal with shapes).
Datasets
Acknowledgements


This is a phenogram, not a phylogenetic tree. For the current best phylogenetic tree, see the Tree of Life project, starting at their primate page. 

In this section we expand our discussion of some topics that were briefly treated due to space constraints.  
In the paper we note that there are
many ways to convert a 2D image into a 1D "time series". Here are just
some examples:


In the paper we discuss Landmarking
("The
idea of “landmarking” is to find the one “true” rotation and only use
that particular alignment as input into the distance measure.").
Here we give some additional examples:
Domain Dependent: For hand shapes Cheng et. al. claims “The principal direction is almost parallel with the direction of finger pointing.” For classifying leaves, Wang et. al. start from the "sharpest corner".
Domain Independent: Gdalyahu and Weinshall tried to select a viable start point by using the distribution of optimal rotation parameters necessary to align anchor point


In the paper we discuss Rotation
Invariant Features. Here we give some additional examples and
pointers:
The paper by Dionisio is a very typical example of what we call in the paper "achieving fast rotation invariant matching by extracting only rotation invariant features and indexing them with a feature vector". The paper by Li et. al. offers an excellent overview of techniques to make shape similarity invariant to a variety of "distortions", including rotation, using Fourier techniques. A fractal based method is presented by Tao et. al.



