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Table 1 The table shows the grouping of the taxonomy features and its idealistic feature values for the creation of the database for automatic classification purpose

From: Discriminative histogram taxonomy features for snake species identification

Feature group

Features

Feature name

Spectacled cobra

King cobra

Common krait

Russel’s viper

Saw scaled viper

Hump nosed pit viper

Top

F 1

Rostral

1

1

1

1

1

1

F 2

Internasal

2

2

2

1

1

2

F 3

Prefrontal

2

2

2

1

1

2

F 4

Supraocular

2

2

2

1

1

2

F 5

Frontal

2

2

2

1

1

2

F 6

Parietals

2

2

2

1

1

2

F 7

V mark on head

0

0

0

1

0

0

F 8

Triangular head

0

0

0

1

0

1

F 9

Two dark patch on head

0

0

0

1

0

0

F 10

Number of scales between Supraoculars

1

1

1

6-9

6-9

1

F 11

Big occipital

0

1

0

0

0

0

F 12

Plus sign in the head

0

0

0

0

1

0

Side

F 13

Small nostril

1

1

1

0

1

1

F 14

Round pupil

1

1

1

0

0

0

F 15

Big nostril

0

0

0

1

0

0

F 16

Elliptical pupil

0

0

0

1

1

1

F 17

Loreal

0

0

0

1

1

1

F 18

Nasorostral

0

0

0

1

0

0

F 19

Supranasal

0

0

0

1

0

0

F 20

Triangular brown streaks below/behind eyes

0

0

0

1

0

0

F 21

Subocular

0

0

0

1

1

1

F 22

Nasal

2

2

2

1

3

1

F 23

Preoculars

1

1

1

4

4

4

F 24

Postoculars

3

3

2

4

4

4

F 25

Supralabial scale

6-7

6-7

6-7

9-11

9-11

9-11

F 38

Pit between eyes and nose

0

0

0

0

0

1

Bottom

F 26

Mental

1

1

1

1

1

1

F 27

Asterior sublingual

1

1

1

1

1

1

F 28

Posterior sublingual

1

1

1

1

1

1

Body

F 29

Round/smooth scale

1

1

1

0

0

0

F 30

Hood

1

0

0

0

0

0

F 31

Spectacled mark on hood

1

0

0

0

0

0

F 32

Keeled scale

0

0

0

1

1

1

F 33

Spots on dorsal scale

0

0

0

1

1

1

F 34

White/yellow stripes on dorsal scale

0

1

1

0

0

0

F 35

Black stripes on ventral scale

0

1

0

0

0

0

F 36

Enlarged and Hexagonal vertebral scale

0

0

1

0

0

0

F 37

Ventral scale

1

1

1

1

1

1

  1. If certain features are visible in the image, corresponding values are assigned else for every invisible or missing feature ‘0’ is assigned.