Clupea Harengus: Intraspecies Distinction using Curvature Scale Space

and Shapelets

Classiﬁcation of North-sea and Thames Herring using Boundary Contour

of Sagittal Otoliths

James Mapp

, Mark Fisher

, Anthony Bagnall

Jason Lines

, Sally Songer

and Joe Scutt Phillips

School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, England, U.K.

CEFAS Laboratory, Pakeﬁeld Road Lowestoft, Suffolk NR33 0HT, U.K.

Keywords:

CSS, Curvature, Scale-space, Shapelets, Otolith, Intraspecies, Classiﬁcation, Random-forrest, Image-

processing, LOOCV, Cross-validation.

Abstract:

We present a study comparing Curvature Scale Space (CSS) representation with Shapelet transformed data

with a view to discriminating between sagittal otoliths of North-Sea and Thames Herring using otolith bound-

ary and boundary metrics. CSS transformed boundaries combined with measures of their circularity, eccen-

tricity and aspect-ratio are used to classify using nearest-neighbour selections with distance being computed

using CSS matching methods. Shapelet transformed data are classiﬁed using a number of techniques (Nearest-

Neighbour, Naive-Bayes, C4.5, Support Vector Machines, Random and Rotation Forest) and compared to CSS

classiﬁcation results. Both methods use Leave One Out Cross Validation (LOOCV). We describe the method

of encoding and the matching algorithm used during CSS classiﬁcation and give an overview of Shapelet

transforms and the classiﬁers that are used on the data. It is shown that whilst CSS forms part of the MPEG-7

standard and performs better than random selection, it can be signiﬁcantly out-performed by recent additions

to machine-learning methods in this application. Shapelets also show that with regard to intra-species dis-

tinction, the discriminatory features of otolith boundaries may lie not in the major inﬂection points, but the

boundary points between them.

1 INTRODUCTION

Otoliths are calcium carbonate structures present in

many vertebrates and found within the sacculus of the

pars inferior. Whilst there are three types of otoliths;

sagittae, lapilli and asterisci, found in each chamber

of ﬁshes, it is primarily the sagittal otoliths that are

used in studies as they are larger and thus easier to

prepare and observe. They vary markedly in shape

and size between species, but are of similar shape

compared to other stocks of the same species (Fig-

ure 1). Otoliths hold information that can be used by

‘expert readers’ to determine several key factors.

Some of these determinations however are harder

to distinguish and (arguably) more critical to ﬁsheries

scientists or ﬁsheries authorities (Begg et al., 2005).

Such management requires that stocks be accurately

determined (Stransky, 2005) so that decisions on their

management can be made. Analysis of otolith bound-

aries may allow estimation of stock composition, de-

termining whether the samples obtained from an area

or areas are all in fact from one stock, or from mul-

tiple stocks mixed together (Duarte-Neto et al., 2008;

Campana and Casselman, 1993; DeVries et al., 2002).

Figure 1: Otoliths from North-Sea Herring (a), Thames

Herring (b) and two distinct populations of Plaice (c and

d).

138

Mapp J., Fisher M., Bagnall A., Lines J., Warne S. and Scutt Phillips J. (2013).

Clupea Harengus: Intraspecies Distinction using Curvature Scale Space and Shapelets - Classiﬁcation of North-sea and Thames Herring using

Boundary Contour of Sagittal Otoliths.

In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, pages 138-143

DOI: 10.5220/0004226101380143

 SciTePress

Currently for more advanced classiﬁcation tasks

such as age determination or stock identiﬁcation,

otoliths samples are often prepared by segmenting

and polishing (Begg et al., 2001; Bolle et al., 2004).

Our study focuses on whether ﬁsh from two stocks

(North-Sea and Thames) of the same species (Her-

ring) can be classiﬁed using otolith boundaries alone.

Collections of otolith specimens were prepared by

the Centre for Environment, Fisheries and Aquacul-

ture Science (CEFAS). The set of images includes

populations of Herring from two distinct stocks,

North-Sea and Thames Herring and have been la-

belled by CEFAS. The set also includes a number of

images of Plaice otoliths which we use when testing

the system. The captured images were received with

no information other than the population to which

they belong.

This paper compares two methods of image

boundary representation: Curvature scale space, part

of the MPEG-7 standard (MPEG-7, 2003); and

Shapelets, a new method ﬁrst proposed by Ea-

monn Keogh’s research group (Ye and Keogh, 2009)

and recently extended by UEA’s machine learning

group (Lines et al., 2012). The CSS-transformed data

are classiﬁed using the standard CSS matching al-

gorithm, whereas the Shapelet data are classiﬁed us-

ing a number of methods; Nearest-Neighbour, Naive-

Bayes, C4.5, Support Vector Machines (linear and

Quadratic), Random Forest and Rotation Forest.

2 SHAPE REPRESENTATION

2.1 Image Segmentation and Boundary

Extraction

Before encoding can be implemented the boundary

of each otolith must be identiﬁed. Images are seg-

mented using simple supervised thresholding scripted

in MATLAB R2010b (MATLAB, 2010). The bound-

ary is then determined by performing a logical ‘OR’

function between the denoised mask and a morpho-

logical dilation of the mask using a square 3x3 struc-

turing element. Each boundary is encoded as a set of

boundary pixel coordinate pairs and stored in a data

structure, with elements representing the coordinate

arrays, image path, and the class. The upper-leftmost

point/pixel on the boundary is used as the start point

and coordinates are extracted in a counter-clockwise

direction in all cases. Boundaries are normalised so

that the origin is located at the boundary centroid, and

subsampled to ﬁve-hundred equally spaced boundary

points.

2.2 CSS Image Construction

Curvature Scale-Space (CSS) (Mokhtarian and Mack-

worth, 1992) forms the basis for contour-based shape

descriptors within MPEG-7 (Bober, 2001; Zhang and

Lu, 2003b; Zhang and Lu, 2003a). As such it forms

an ideal starting point for boundary based shape clas-

siﬁcation of otoliths and has been used for several

other studies in this ﬁeld (Parisi-Baradad et al., 2005;

Abbasi et al., 1999; Jalba et al., 2006). Research has

shown that CSS encoding and its attributed matching

algorithm can be an effective and robust (to noise,

scale, rotation) method of matching query images

to database models when combined with global pa-

rameters such as Circularity and Eccentricity (Abbasi

et al., 1999; Amanatiadis et al., 2011).

In addition to intraspecies distinction, we also dis-

criminate between two species of ﬁsh (Herring and

Plaice) and on classes from the MPEG-7 SHAPE

database (MPEG7, 1999) with a view to benchmark-

ing our CSS implementation. The SHAPE database

contains 20 images per class for 70 classes, and was

developed for testing MPEG-7 shape descriptors. For

use in our study we remove the ‘device’ classes as

they hold deliberate within class variance. We also

exclude the ‘spring’ class as the shape itself causes

problems when creating CSS data.

The CSS images are constructed using the proce-

dure laid out in previous work (Abbasi et al., 1999).

The process involves iteratively smoothing the origi-

nal boundary curve of the image using a 1-D Gaussian

function of increasing kernel width and, after each it-

eration, computing the curvature of the boundary and

where the curvature ‘crosses’ zero. This process con-

tinues until there are no remaining zero-crossings on

the boundary, and stored as coordinate pairs (maxima)

representing the points along the boundary and the it-

eration or “evolution” at which zero-crossings con-

verge.

As in previous work we initiated the process us-

ing a kernel width (σ) of 1, increasing by 0.1 after

each evolution, and compute a small number of global

metrics to assist in the matching process (Mokhtarian

et al., 1996; Abbasi et al., 1999): Circularity and Ec-

centricity (of the boundary); and Aspect-Ratio (of the

CSS image created during the process).

2.3 Time Series Shapelets

A Shapelet is a time series data mining primitive

that can be used to measure similarity between series

based on small common shapes that occur at any point

in the series. The original work on Shapelets was pre-

sented in (Ye and Keogh, 2009), where a recursive

ClupeaHarengus:IntraspeciesDistinctionusingCurvatureScaleSpaceandShapelets-ClassificationofNorth-seaand

ThamesHerringusingBoundaryContourofSagittalOtoliths

139

decision tree algorithm is presented using Shapelets

as the branching criteria. Of particular interest is the

work in (Lines et al., 2012), where it is proposed

that Shapelets can be used to construct a ﬁlter for

transforming time series data. Transforming data in

this manner moves away from the previous emphasis

of tree-based classiﬁcation, allowing any traditional

classiﬁcation algorithm to be used with Shapelets.

We use the Java implementation of (Lines et al.,

2012) to build a ﬁlter for transforming the Herring

boundary data. The implementation utilises infor-

mation gain to test the discriminatory power of a

Shapelet. To initialise the ﬁlter, three parameters are

required: minimum/maximum Shapelet lengths, and

the number of Shapelets to be extracted. We ex-

tracted the best 100 Shapelets between the lengths of

40 and 120 from the dataset for the transformation

process. This transformation is then carried out for

each boundary by calculating the distance from the

series to each of the extracted Shapelets, and each dis-

tance is used as an attribute in the transformed data.

3 CLASSIFICATION

3.1 CSS Classiﬁcation

With each boundary stored as the locations of its

curvature-crossing maxima, images can be compared

to one another using previously deﬁned methods (Ab-

basi et al., 1999; Mokhtarian et al., 1996). This re-

sults in a measure of similarity between the two sets

of maxima and can be used to compare one image

to a number of models in a database and ﬁnd the

model with the greatest similarity. We are able to

dismiss a number of models by comparing the CSS

image/model aspect-ratios as well as the circularity

and eccentricity metrics, dismissing as dissimilar any

pairs with metric ratios greater than a given thresh-

old (T).

Tests are performed using multiple values for T

on both the Herring and SHAPE datasets. Initially

it was thought that T may be pattern independent.

The results show that optimum T values are signiﬁ-

cantly different between the two data sets, and use of

one on the other set would result in signiﬁcant under-

performance. As in (Abbasi et al., 1999) we set al-

lignments for all maxima ≥ 80% of the largest evolu-

tion level, however we use a more generous matching

distance at 40% of maximum distance.

CSS classiﬁcation is performed using Nearest-

Neighbour (NN) selection, with model distances

equal to their dissimilarity. The set of images is

tested using LOOCV and results returned as a per-

centage of selections that resulted in the correct class.

Classiﬁcation using the same methods are carried out

on images from the MPEG-7 SHAPE database to

benchmark our CSS implementation. For compari-

son we also process otoliths of Plaice (Pleuronectes

platessa) alongside Herring otoliths (HvP) to deter-

mine whether inter-species distinction is possible us-

ing the same method.

3.2 Boundary and Shapelet

Classiﬁcation

We use LOOCV when classifying both the Uni-

variate Boundaries (UV-Bs) and Shapelet transform

thereof, using a variety of available classiﬁers so that

results of transformed and non-transformed bound-

aries can be compared. CSS maxima (as Boundary-

Point/Evolution pairs) are tested using the same clas-

siﬁers in addition to using the standard CSS matching

procedure in order to evaluate the matching method

itself. Two datasets are created for this testing; CSSa

(maxima sorted by distance along the boundary) and

CSSb (by evolution magnitude). The classiﬁers used

on these sets are implemented in the Java Machine

Learning toolkit WEKA (Hall et al., 2009) and are;

1-Nearest Neighbour with Euclidean distance (NN),

Naive Bayes (NB), C4.5 Decision Tree (C4.5), Sup-

port Vector Machines with a linear/quadratic ker-

nels (SVML/SVMQ), Random Forest ensemble with

100 base classiﬁers (RaF), Rotation Forest ensemble

with 30 base classiﬁers (RoF), 1-Nearest Neighbour

with dynamic time warping distance, performed on

UV-OL data only (NNDTW).

4 RESULTS

4.1 CSS Classiﬁcation

Results from classiﬁcation using the CSS matching

implementation can be seen in Figures 2 and 3. The

ﬁgures show results given eccentricity, circularity and

aspect-ratio thresholds in the range 0 to 0.40. It can be

seen that the thresholds returning peak performance in

these cases are signiﬁcantly different to one another,

and use of one problem’s optimised threshold value

for the other would result in under-performance.

In all cases the inclusion of a threshold for the

global parameters improve, or at least do not hinder,

accuracy. Table 1 shows results of the two classiﬁca-

tions using no threshold (where T=1), and when us-

ing the peak performance global threshold (value for

ICPRAM2013-InternationalConferenceonPatternRecognitionApplicationsandMethods

140

Figure 2: Graph showing results of NorthSea/Thames Her-

ring classiﬁcation using 1, 3 and 5-NN over multiple thresh-

old values.

Figure 3: Graph showing results of SHAPE database image

classiﬁcation using 1, 3 and 5-NN over multiple threshold

values.

each case given in table). Alongside are shown results

when classifying Herring/Plaice otoliths (interspecies

distinction) both with and without peak values for T.

Table 2 shows sensitivity and speciﬁcity (or Sen-

sitivity NS and Sensitivity Th) of the CSS matching

algorithm. The ﬁgures show that the CSS matching

technique is generally more sensitive to Thames Her-

ring otoliths than to North Sea otoliths. The table

shows results for using both peak performance thresh-

old, and no threshold (divided by the double vertical

lines).

The increase in accuracy, sensitivity and speci-

ﬁcity when global thresholds are implemented sup-

ports the idea that the two classes of boundary have

signiﬁcant overlap in scale space, where boundaries

that show differences while unprocessed may have

much the same representation.

Table 1: The LOOCV classiﬁcation accuracies using our

CSS matching implementation on all three tests, North-Sea

Vs Thames (NSvTh), Classes from the SHAPE database

(SHAPE) and Herring Vs Plaice (HvP).

1-NN 3-NN 5-NN

NSvTh T = 1 55% 56% 50%

peak T = 0.01 61% 61% 45%

SHAPE T = 1 87% 69% 59%

peak T = 0.25 91% 74% 67%

HvP T = 1 100% 99% 100%

peak T = 0.20 100% 100% 100%

Table 2: Confusion matrices including sensitivity (Se) and

Speciﬁcity (Sp) for NSvTh classiﬁcation using 1,3 and 5-

NN selection (Rows - Query, Columns - Result). Table to

the Left using peak T value, to the right using no threshold

(T=1).

NS Th Se/Sp NS Th Se/Sp

1-NN NS 26 24 0.52 21 29 0.42

Th 15 35 0.70 16 34 0.68

3-NN NS 29 21 0.58 25 25 0.50

Th 18 32 0.64 19 31 0.62

5-NN NS 24 26 0.48 25 25 0.50

Th 29 21 0.42 25 25 0.50

4.2 Shapelet Classiﬁcation

The results in Table 3 demonstrate promising classi-

ﬁcation results using the Shapelet-transformed data.

The best performing classiﬁer on this data is the Ran-

dom Forest algorithm, which was over 20% more ac-

curate than using a simple one-nearest neighbour ap-

proach. It is also interesting to note that the stan-

dard decision tree implementation using C4.5 is out-

performed by the simple Naive Bayes classiﬁer. The

Table also shows results of the same classiﬁcation al-

gorithms using Univariate Boundary-data (UV-B) and

CSS maxima ‘coordinate’ pairs (CSSa and CSSb).

Table 3: The LOOCV classiﬁcation accuracies of a range

of algorithms using Univariate Boundary data (UV-B),

Shapelet transformed data (Shapelet) and CSS maxima

(CSSa/CSSb).

Classiﬁer UV-B Shapelet CSSa CSSb

NN 58% 64% 55% 49%

NB 63% 77% 55% 50%

C4.5 56% 74% 46% 56%

SVML 59% 75% 65% 70%

SVMQ 58% 71% 59% 67%

RaF 68% 87% 54% 54%

RoF 61% 78% 52% 52%

NN - DTW 65% N/A N/A N/A

ClupeaHarengus:IntraspeciesDistinctionusingCurvatureScaleSpaceandShapelets-ClassificationofNorth-seaand

ThamesHerringusingBoundaryContourofSagittalOtoliths

141

From the top ﬁve Shapelets it was seen that the dis-

criminating features determined by Shapelet encod-

ing fall in areas of very low curvature. This may

explain the comparatively poor results produced by

the CSS method on HvH tests, where the same sys-

tem performs well on other datasets. If discrimina-

tory portions of the boundary mainly fall in areas of

low curvature, then curvature modelling techniques

will have difﬁculty distinguishing between the two

classes. Also where discriminatory features depend

on angle of section with respect to the boundary cen-

troid or other sections of the curve, CSS methods

will be unable to distinguish between them as curva-

ture maxima give no indication of angle with respect

to non-regional areas. It is noted however that us-

ing curvature maxima did offer some improvement on

random selection, therefore maxima must offer some

level of discriminatory power.

5 CONCLUSIONS

Our study has shown that curvature scale space works

well for classiﬁcation where class boundaries are

signiﬁcantly different (between species or between

classes of the SHAPE database). Nearest-Neighbour

classiﬁcation of Herring performed better using UV-

Bs than the CSS transformed data alone (CSSa, CSSb

in Table 3), the use of CSS’s own matching algo-

rithm and global thresholds increase accuracy fur-

ther, to around the same accuracy as other classiﬁca-

tion methods (of UV-Bs). Even the best CSS encod-

ing/classifying method is signiﬁcantly outperformed

by Shapelet method of encoding regardless of which

classiﬁer is used to process data. The method offers

accuracy (3-26% better), sensitivity and speciﬁcity far

above the CSS matching methods used.

Our results compare well with previous studies

of stock discrimination using otoliths. Studies of

dolphinﬁsh otoliths (Duarte-Neto et al., 2008) us-

ing fourier desciptors of the boundary show results

in the region 57-70% which is comparable to our

CSS matching implementation. Campana and Cas-

selman (Campana and Casselman, 1993) produce re-

sults of 67% using otolith boundary alone, however

other results in the same study fall far below those

discussed in this work. Results from mackerel classi-

ﬁcation (DeVries et al., 2002) show 80-86% accuracy

which while signiﬁcantly above our CSS implemen-

tation, compares well with our shapelet classiﬁcation

results. Our shapelet approach also matches the ac-

curacies of studies which use microstructure analy-

sis (Petursdottir et al., 2006), and where shape alone

returned results equal to or lower than our CSS meth-

ods.

Results from tests on the SHAPE database com-

pare well with previous work (Abbasi et al., 1999;

Latecki et al., 2000). Whilst our results have shown

a moderate (15%) improvement on those results it

should be noted that we have excluded several classes

from our SHAPE set, and some previous results are

given with restricted sets themselves.

6 FURTHER WORK

For the purpose of this study we have returned re-

sults using multiple values for the global threshold.

However this threshold itself is used for three dif-

ferent global measures; circularity, eccentricity and

maxima aspect-ratio, rather than each having their

own limit. Further work should attempt to better

utilise the three metrics. Existing and subsequent

metrics should be assessed individually for effective-

ness rather than using a single threshold. Whilst we

have determined that the threshold cannot be set using

separate (SHAPE) datasets, the threshold itself should

be set using some form of double-cross validation if

possible (Stone, 1974) to avoid overﬁtting the thresh-

old or thresholds to the speciﬁc classiﬁcation prob-

lem.

As Shapelets appear to show that important in-

formation is available in areas of low curvature, it

is possible that methods to enhance CSS for shapes

with shallow concavities (Abbasi et al., 2000) may

increase classiﬁcation accuracy. We plan to compli-

ment the method used with these enhancements to de-

termine whether they are suitable for our problem. It

may be further possible that other enhancement meth-

ods such as those that encode convex shapes (Kopf

et al., 2005) improve results using the CSS matching

technique.

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ClupeaHarengus:IntraspeciesDistinctionusingCurvatureScaleSpaceandShapelets-ClassificationofNorth-seaand

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143