PhotoId-Whale: Blue whale dorsal fin classification for mobile devices.

Rosa I Ramos-Arredondo,J B Rosas-Fernández,Blanca E Carvajal-Gámez,Dante Mújica-Vargas,Francisco J Gallegos-Funes,Diane Gendron,Songhai Li

doi:10.1371/journal.pone.0237570

Rosa I Ramos-Arredondo, J B Rosas-Fernández + Show 5 more

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https://doi.org/10.1371/journal.pone.0237570

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Abstract

Photo-identification (photo-id) is a method used in field studies by biologists to monitor animals according to their density, movement patterns and behavior, with the aim of predicting and preventing ecological risks. However, these methods can introduce subjectivity when manually classifying an individual animal, creating uncertainty or inaccuracy in the data as a result of the human criteria involved. One of the main objectives in photo-id is to implement an automated mechanism that is free of biases, portable, and easy to use. The main aim of this work is to develop an autonomous and portable photo-id system through the optimization of image classification algorithms that have high statistical dependence, with the goal of classifying dorsal fin images of the blue whale through offline information processing on a mobile platform. The new proposed methodology is based on the Scale Invariant Feature Transform (SIFT) that, in conjunction with statistical discriminators such as the variance and the standard deviation, fits the extracted data and selects the closest pixels that comprise the edges of the dorsal fin of the blue whale. In this way, we ensure the elimination of the most common external factors that could affect the quality of the image, thus avoiding the elimination of relevant sections of the dorsal fin. The photo-id method presented in this work has been developed using blue whale images collected off the coast of Baja California Sur. The results shown have qualitatively and quantitatively validated the method in terms of its sensitivity, specificity and accuracy on the Jetson Tegra TK1 mobile platform. The solution optimizes classic SIFT, balancing the results obtained with the computational cost, provides a more economical form of processing and obtains a portable system that could be beneficial for field studies through mobile platforms, making it available to scientists, government and the general public.

Highlights

The first documented investigations of pattern recognition for animals began with the bottlenose dolphin on the coast of the Gulf of Mexico by researcher David K
When we developed the test scenarios, four metrics were considered: true positive (TP) and true negative (TN)—which are for correct classifications—and false positive (FP) and false negative (FN)—which are for incorrect classifications; by using these metrics, we can obtain different performance measures as follows [46]: Sp 1⁄4 TN
Among the photographs in the blue whale image database, 57.2% were taken from both sides of the whale, 23.8% were taken from the right flank and 19.0% were taken from the left flank

Summary

Introduction

The first documented investigations of pattern recognition for animals began with the bottlenose dolphin on the coast of the Gulf of Mexico by researcher David K. The Scale Invariant Feature Transforms (SIFTs) are usually vectors with a large number of components, which can generate redundant information or contain similar points or very close values between them, with a high computational cost [34,35] These points are extracted from the contour of the dorsal fin of the blue whale on both flanks. Innovation in new technologies and the accessibility of sensors, social networks, hi-fi cameras, interoperability between various devices and platform, storage capabilities, cloud computing, and computing on mobile development platform all together are powerful tools that are currently available on mobile platform, as shown in [41] They are available to any researcher, naturalist, biologist or the general public, and there are applications that help or collaborate on information collection [42]. In the case of this research, we validated the results on the Jetson Tegra TK1 mobile development platform, which has several advantages such as the following: low cost, low power consumption and high applicability [44,45]

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