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Image Features and Neural Network Classifiers for Animal
Behaviour Recognition
Carlos Fernando Crispim Junior, BCSDoctorate student
Advisor: José Marino Neto, Dr.Sc.Co-Advisor: Fernando Mendes de Azevedo, Dr.Sc.
2007
2/18
What is behaviour?
3/18
Sistematic Behaviour Score
Ethograph
PharmacologyNeuroscience
Video-trackingLesion
LEHNER (1996)
4/18
Automatic Behaviour Score
Locomotion Imobility Grooming
Behavioural stream
Locomotion
LABORAS™
Vibration
seconds
A
B
5/18
Classification of rat behaviour with animage-processing method and a neural network
• RNA Multilayer Perceptron (MLP)
• Morphologic descriptors[animal postures]
27 descriptors 3 frames per sample
ROUSSEAU et al. (2000)
6/18
Summary
• Few image processing solution using image descriptors (for behavioural phenomena);
• Almost no use of kinematic descriptors;
• Lack of evaluation of behavioural descriptors for relevance;
• Low focus on the Temporal relationship among a set of frames
• How to evaluate ANN classifiers without golden pattern;
7/18
General Objective
Study the use of image features (behavioural descriptors) and descriptive statistics attributes to describe behavioural events of lab animals, and automatically identify them using artificial neural networks (ANN), evaluated by different performance indexes.
8/18
EthoWatcher®: behaviour score
LEHNER (1996)
Time (seconds) Behaviour
Behaviour Frequency Duration Latency
Locomotion 2 90 s 10 s
Risk assesment 1 60 s 60 s
Immobillity 1 95 s 95 s
Rearing 1 XXX 120s
[010 s] Locomotion[060 s] Risk assesment[080 s] Locomotion[095 s] Immobillity[120 s] Rearing
9/18
Rat naive to treatment
Rat treated with caffeine
EthoWatcher®: Activity Analysis
10/18
Frame 329
Animal length Distance travelled[kinematic descriptor]
Animal area[morphological descriptor]
Number of modified pixels[kinematic descriptor]
Frame 328
Animal orientation angle[morphological descriptor]
Animal estimated position
Behavioural descriptors
11/18
Frame 329
Frame 330
Behavioural time-relationship profile
Frame 331
Fonte: Benjamini et al., 2010
Descriptive Statistics Attributes Mean; Mode; Variance; Skewness; Kurtosis; e etc.
12/18
Statistical AnalysisDescritor=NpxPosSubtracao
Boxplot by Group
Variable: Std.Dev.
Mean Mean±SE Mean±SD
LocomocaoExp.Vertical
ImobilidadeExp.Horizontal
Auto-limpeza
Comportamento
0
50
100
150
200
250
300
350
Std
.Dev.
Descritor=VariacaoAngularBoxplot by Group
Variable: Mean
Mean Mean±SE Mean±SD
LocomocaoExp.Vertical
ImobilidadeExp.Horizontal
Auto-limpeza
Comportamento
-4
-2
0
2
4
6
8
10
12
14
16
Mean
• One-way analysis of variance - ANOVA (parametric)• One-way Kruskal-Wallis (non-
parametric)
Which behavioural descriptors are relevant?
13/18
Descriptive Statistics DistributionBehaviour=Grooming, Descriptor=Animal Area
Histogram: Mean
Expected Normal76
5,99
5777
8,35
0579
0,70
5380
3,06
0181
5,41
4882
7,76
9684
0,12
4485
2,47
9186
4,83
3987
7,18
8788
9,54
3490
1,89
8291
4,25
3092
6,60
7893
8,96
2595
1,31
7396
3,67
2197
6,02
6898
8,38
1610
00,7
364
1013
,091
110
25,4
459
1037
,800
710
50,1
555
1062
,510
210
74,8
650
1087
,219
810
99,5
745
1111
,929
311
24,2
841
1136
,638
811
48,9
936
1161
,348
411
73,7
032
1186
,057
911
98,4
127
1210
,767
512
23,1
222
1235
,477
012
47,8
318
1260
,186
5
X <= Category Boundary
0
1
2
3
No.
of o
bs.
14/18
Descriptive Statistics DistributionBehaviour=Rearing, Descriptor=Number of Modified Pixels
Histogram: Variance
Expected Normal15
26,0
8430
52,1
6845
78,2
5261
04,3
3676
30,4
2091
56,5
0410
682,
588
1220
8,67
213
734,
756
1526
0,84
016
786,
924
1831
3,00
819
839,
092
2136
5,17
622
891,
260
2441
7,34
425
943,
428
2746
9,51
228
995,
596
3052
1,68
032
047,
764
3357
3,84
835
099,
932
3662
6,01
638
152,
100
3967
8,18
441
204,
268
4273
0,35
244
256,
436
4578
2,52
047
308,
604
4883
4,68
850
360,
772
5188
6,85
653
412,
940
5493
9,02
456
465,
108
5799
1,19
259
517,
276
6104
3,36
062
569,
444
X <= Category Boundary
0
1
2
3
4
5
6
7
No.
of o
bs.
15/18
ANN input
Attribute of Descriptor 01
Ex.: ANN Locomotion
Attribute of Descriptor 02
Attribute of Descriptor 03
Attribute of Descriptor n
RELEVANT descriptors for Locomotion identification
Original behaviour sample length
Example: ½, 1, 2 seconds.
• Locomotion
• Imobility
• Grooming
• Rearing
Multi-layer perceptron
16/18
– Kappa coefficient, – ROC Curves and Area under ROC Curve (AUC).– Accuracy. 1
10
1 - Especificity
Sens
ibili
tyOperation dotAUC
Classifiers evaluation
17/18
Ongoing activities• Determination of Statistical procedures power for descriptors relevance identification;
(in progress).
•Analysis ANN classifiers performance and best training epochs;
(in progress);
• Evaluation of Descriptive Statistics attributes length influences on events
identification;
(in progress);
Image Features and Neural Network Classifiers for Animal
Behaviour Recognition
Carlos Fernando Crispim Junior, [email protected]