Step patterns for DTW

Description

A stepPattern object lists the transitions allowed while searching for the minimum-distance path. DTW variants are implemented by passing one of the objects described in this page to the stepPattern argument of the dtw call.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
## Well-known step patterns
  symmetric1
  symmetric2
  asymmetric

## Step patterns classified according to Rabiner-Juang [Rabiner1993]
  rabinerJuangStepPattern(type,slope.weighting="d",smoothed=FALSE)

## Slope-constrained step patterns from Sakoe-Chiba [Sakoe1978]
  symmetricP0;  asymmetricP0
  symmetricP05; asymmetricP05
  symmetricP1;  asymmetricP1
  symmetricP2;  asymmetricP2

## Step patterns classified according to Rabiner-Myers [Myers1980]
  typeIa;   typeIb;   typeIc;   typeId;
  typeIas;  typeIbs;  typeIcs;  typeIds;  # smoothed
  typeIIa;  typeIIb;  typeIIc;  typeIId;
  typeIIIc; typeIVc;

## Miscellaneous
  mori2006;
  rigid;

## S3 method for class 'stepPattern'
print(x,...)
## S3 method for class 'stepPattern'
plot(x,...)
## S3 method for class 'stepPattern'
t(x)

stepPattern(v,norm=NA)
is.stepPattern(x)

Arguments

x

a step pattern object

type

path specification, integer 1..7 (see [Rabiner1993], table 4.5)

slope.weighting

slope weighting rule: character "a" to "d" (see [Rabiner1993], sec. 4.7.2.5)

smoothed

logical, whether to use smoothing (see [Rabiner1993], fig. 4.44)

v

a vector defining the stepPattern structure

norm

normalization hint (character)

...

additional arguments to print.

Details

A step pattern characterizes the matching model and slope constraint specific of a DTW variant. They also known as local- or slope-constraints, transition types, production or recursion rules [GiorginoJSS].

print.stepPattern prints an user-readable description of the recurrence equation defined by the given pattern.

plot.stepPattern graphically displays the step patterns productions which can lead to element (0,0). Weights are shown along the step leading to the corresponding element.

t.stepPattern transposes the productions and normalization hint so that roles of query and reference become reversed.

A variety of classifications have been proposed for step patterns, including Sakoe-Chiba [Sakoe1978]; Rabiner-Juang [Rabiner1993]; and Rabiner-Myers [Myers1980]. The dtw package implements all of the transition types found in those papers, with the exception of Itakura's and Velichko-Zagoruyko's steps which require subtly different algorithms (this may be rectified in the future). Itakura recursion is almost, but not quite, equivalent to typeIIIc.

For convenience, we shall review pre-defined step patterns grouped by classification. Note that the same pattern may be listed under different names. Refer to paper [GiorginoJSS] for full details.

1. Well-known step patterns

These common transition types are used in quite a lot of implementations.

symmetric1 (or White-Neely) is the commonly used quasi-symmetric, no local constraint, non-normalizable. It is biased in favor of oblique steps.

symmetric2 is normalizable, symmetric, with no local slope constraints. Since one diagonal step costs as much as the two equivalent steps along the sides, it can be normalized dividing by N+M (query+reference lengths).

asymmetric is asymmetric, slope constrained between 0 and 2. Matches each element of the query time series exactly once, so the warping path index2~index1 is guaranteed to be single-valued. Normalized by N (length of query).

2. The Rabiner-Juang set

A comprehensive table of step patterns is proposed in Rabiner-Juang's book [Rabiner1993], tab. 4.5. All of them can be constructed through the rabinerJuangStepPattern(type,slope.weighting,smoothed) function.

The classification foresees seven families, labelled with Roman numerals I-VII; here, they are selected through the integer argument type. Each family has four slope weighting sub-types, named in sec. 4.7.2.5 as "Type (a)" to "Type (d)"; they are selected passing a character argument slope.weighting, as in the table below. Furthermore, each subtype can be either plain or smoothed (figure 4.44); smoothing is enabled setting the logical argument smoothed. (Not all combinations of arguments make sense.)

Subtype Rule Norm Unbiased
a min step -- NO
b max step -- NO
c Di step N YES
d Di+Dj step N+M YES

3. The Sakoe-Chiba set

Sakoe-Chiba [Sakoe1978] discuss a family of slope-constrained patterns; they are implemented as shown in page 47, table I. Here, they are called symmetricP<x> and asymmetricP<x>, where <x> corresponds to Sakoe's integer slope parameter P. Values available are accordingly: 0 (no constraint), 1, 05 (one half) and 2. See [Sakoe1978] for details.

4. The Rabiner-Myers set

The type<XX><y> step patterns follow the older Rabiner-Myers' classification proposed in [Myers1980] and [MRR1980]. Note that this is a subset of the Rabiner-Juang set [Rabiner1993], which should be preferred in order to avoid confusion. <XX> is a roman numeral specifying the shape of the transitions; <y> is a letter in the range a-d specifying the weighting used per step, as above; typeIIx patterns also have a version ending in s, meaning the smoothing is used (which does not permit skipping points). The typeId, typeIId and typeIIds are unbiased and symmetric.

5. Other

The rigid pattern enforces a fixed unitary slope. It only makes sense in combination with open.begin=T, open.end=T to find gapless subsequences. It may be seen as the P->inf limiting case in Sakoe's classification.

mori2006 is Mori's asymmetric step-constrained pattern [Mori2006]. It is normalized by the matched reference length.

Note

Constructing stepPattern objects is tricky and thus undocumented. For a commented example please see source code for symmetricP1.

Author(s)

Toni Giorgino

References

[GiorginoJSS] Toni Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1-24. http://www.jstatsoft.org/v31/i07/

[Itakura1975] Itakura, F., Minimum prediction residual principle applied to speech recognition, Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on , vol.23, no.1, pp. 67-72, Feb 1975. URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1162641

[MRR1980] Myers, C.; Rabiner, L. & Rosenberg, A. Performance tradeoffs in dynamic time warping algorithms for isolated word recognition, IEEE Trans. Acoust., Speech, Signal Process., 1980, 28, 623-635. URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1163491&tag=1

[Mori2006] Mori, A.; Uchida, S.; Kurazume, R.; Taniguchi, R.; Hasegawa, T. & Sakoe, H. Early Recognition and Prediction of Gestures Proc. 18th International Conference on Pattern Recognition ICPR 2006, 2006, 3, 560-563. URL: http://dx.doi.org/10.1109/ICPR.2006.467

[Myers1980] Myers, C. S. A Comparative Study Of Several Dynamic Time Warping Algorithms For Speech Recognition, MS and BS thesis, MIT Jun 20 1980, dspace.mit.edu/bitstream/1721.1/27909/1/07888629.pdf

[Rabiner1993] Rabiner, L. R., & Juang, B.-H. (1993). Fundamentals of speech recognition. Englewood Cliffs, NJ: Prentice Hall.

[Sakoe1978] Sakoe, H.; Chiba, S., Dynamic programming algorithm optimization for spoken word recognition, Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on , vol.26, no.1, pp. 43-49, Feb 1978 URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1163055

See Also

mvmStepPattern, implementing Latecki's Minimal Variance Matching algorithm.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
#########
##
## The usual (normalizable) symmetric step pattern
## Step pattern recursion, defined as:
## g[i,j] = min(
##      g[i,j-1] + d[i,j] ,
##      g[i-1,j-1] + 2 * d[i,j] ,
##      g[i-1,j] + d[i,j] ,
##   )

print(symmetric2)   # or just "symmetric2"



#########
##
## The well-known plotting style for step patterns

plot(symmetricP2,main="Sakoe's Symmetric P=2 recursion")



#########
##
## Same example seen in ?dtw , now with asymmetric step pattern

idx<-seq(0,6.28,len=100);
query<-sin(idx)+runif(100)/10;
reference<-cos(idx);

## Do the computation 
asy<-dtw(query,reference,keep=TRUE,step=asymmetric);

dtwPlot(asy,type="density",main="Sine and cosine, asymmetric step")


#########
##
##  Hand-checkable example given in [Myers1980] p 61
##

`tm` <-
structure(c(1, 3, 4, 4, 5, 2, 2, 3, 3, 4, 3, 1, 1, 1, 3, 4, 2,
3, 3, 2, 5, 3, 4, 4, 1), .Dim = c(5L, 5L))