Texture Classification of Sea Turtle Shell based on Color Features: Color Histograms and Chromaticity Moments

Abstract

A collaborative system for cataloging sea turtles activity that supports picture/video content demands automated solutions for data classification and analysis. This work assumes that the color characteristics of the carapace are sufficient to classify each species of sea turtles, unlikely to the traditional method that classifies sea turtles manually based on the counting of their shell scales, and the shape of their head. Particularly, the aim of this study is to compare two features extraction techniques based on color, Color Histograms and Chromaticity Moments, combined with two classification methods, K-nearest neighbors (KNN) and Support Vector Machine (SVM), identifying which combination of techniques has a higher effectiveness rate for classifying the five species of sea turtles found along the Brazilian coast. The results showed that the combination using Chromaticity Moments with the KNN classifier presented quantitatively better results for most species of turtles with global accuracy value of 0.74 and accuracy of 100% for the Leatherback sea turtle, while the descriptor of Color Histograms proved to be less precise, independent of the classifier. This work demonstrate that is possible to use a statistical approach to assist the job of a specialist when identifying species of sea turtle.