PhotoId-Whale: Blue whale dorsal fin classification for mobile devices
Rosa I. Ramos-Arredondo, Investigation , # 1, ¤a ‡ Blanca E. Carvajal-Gámez, Funding acquisition , Investigation , Supervision , Writing – original draft , # 2, ¤b ‡ * Diane Gendron, Conceptualization , 3, ‡ Francisco J. Gallegos-Funes, Visualization , Writing – review & editing , 4, ‡ Dante Mújica-Vargas, Methodology , Visualization , Writing – review & editing , 5, ‡ and J. B. Rosas-Fernández, Writing – review & editing 6, ‡
Rosa I. Ramos-Arredondo
1 SEPI-Culhuacan, Instituto Politécnico Nacional, Ciudad de México, México
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Blanca E. Carvajal-Gámez
2 SEPI-ESCOM, Instituto Politécnico Nacional, Ciudad de México, México
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Diane Gendron
3 CICIMAR, Instituto Politécnico Nacional, Ciudad de México, México
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Francisco J. Gallegos-Funes
4 SEPI-Zacatenco, Instituto Politécnico Nacional, Ciudad de México, México
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Dante Mújica-Vargas
5 Departamento de Ciencias Computacionales, Tecnológico Nacional de México, Cuernavaca-Morelos, México
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J. B. Rosas-Fernández
6 Secretaría de Educación, Ciencia, Tecnología e Innovación, Ciudad de México, México
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Songhai Li, Editor
1 SEPI-Culhuacan, Instituto Politécnico Nacional, Ciudad de México, México
2 SEPI-ESCOM, Instituto Politécnico Nacional, Ciudad de México, México
3 CICIMAR, Instituto Politécnico Nacional, Ciudad de México, México
4 SEPI-Zacatenco, Instituto Politécnico Nacional, Ciudad de México, México
5 Departamento de Ciencias Computacionales, Tecnológico Nacional de México, Cuernavaca-Morelos, México
6 Secretaría de Educación, Ciencia, Tecnología e Innovación, Ciudad de México, México
Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, CHINA
# Contributed equally.
Competing Interests: The authors have declared that no competing interests exist.
¤a Current address: SEPI-Culhuacan, ESIME-Culhuacan, Ciudad de México, México
¤b Current address: ESCOM, Ciudad de México, México
‡ These authors also contributed equally to this work
* E-mail: xm.npi@lajavraceb, moc.liamg@glajavracebard
Received 2019 Oct 17; Accepted 2020 Jul 30.
Copyright © 2020 Ramos-Arredondo et al
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Associated Data
The data cannot be shared publicly because the blue whale image database belongs to CICIMAR-IPN, it must be requested directly from these instances. The Database of images of the Blue Whale belongs to CICIMAR-IPN and who collects, manages, administers the information is Dr. Diane Gendron, who is co-author of the manuscript, with these instances they must assist to access the Blue Whale Image Database. To follow the access path correctly, please initiate communication in the following order: academic direction-CICIMAR: xm.npi@ramicicnoiccerid and the academic subdirection-CICIMAR: xm.npi@acimedacabus and you can contact Dr. Gendron through her email: xm.npi@nordnegd, Laboratory of Ecology of Cetaceans and Quelonios.
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.
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. Caldwell in 1955 [1], but it was not until 1970 that the observation of cetaceans used this technique. There are research works that study photo identification. Currently, the photo-id method is used in a wide variety of studies such as those focused on life cycles, group structures, geographical characteristics, population structures, population size estimates, and migrations, among others [2–9]. In the case of cetaceans, the marks used for photo-id may be caused by parasites, predator attacks, conspecifics, anthropogenic activities and congenital conditions [10]. However, these marks can be modified over time; consequently, other characteristics have been considered as identification mechanisms. The dorsal fin contour or marks from injuries are some, which generally are among the most accurate features to use for photo-id studies as demonstrated in previous work [10]. Photo-id is a minimally invasive technique [11], which has the capacity to demonstrate the potential consequences of human impacts and/or management actions by quantifying the spatial relationships between populations and the variables that define or reflect their habitat preferences [12]. The application of photo-id and electronic tagging in field studies has shown a relatively high degree of site fidelity [13], which is useful in order to establish possible conservation strategies. Systems based on computer vision, which are focused on image classification, have long attracted the attention of image processing researchers because the results obtained form the basis of many environmental and socioeconomic applications [14]. However, the classification of images acquired in uncontrolled media through surveillance cameras remains a recurring topic of study due to many factors, such as the complexity of the landscape in the study area, the various performance characteristics of the camera, the size and movement of the subject, the framing, and the camera’s sensitivity, which can affect the success of the classification result [15–17]. Most photo-id procedures consist of three steps. The first step is the manual selection and trimming of an area of interest to the individual within the image, after which the person in charge of the photo-id classifies and catalogs the images according to their criteria or experience. The second step is an automated algorithmic comparison between the sample and an image library that rates candidates by their coincident probability. The final step is a visual comparison of sample-candidate pairs to confirm positive matches [18]. Photo-id software has been developed to facilitate the task of identifying a diverse set of species, such as in [19–31]. Each of these photo-id systems have been developed on a laptop and personal computer due to the complexity of the proposals developed, such as the work shown in [32] that compares four animal photo-id systems. In the case of the blue whale, an identifying characteristic is the shape of its dorsal fin observed on both the right and left flanks. The dorsal fin of the blue whale can be grouped according to its shape; this classification was proposed by Gendron et al. [33], who explained that photographs taken of the dorsal fin must be observed on both flanks. Because the photo-id systems referenced above requires the help of an expert to detect the characteristics points, the resulting classification may contain errors or a certain bias and, consequently, may vary for the same individual. To identify an animal, it is necessary to extract its main characteristic transforms. For example, 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. We propose the elimination of coincident characteristic points using statistical discriminators such as the variance, standard deviation, mode and cross-correlation. Then, the results will verify that the data that were not discriminated provide sufficient information to carry out the identification and classification of the shape of the dorsal fin, resulting in an economical, fast and feasible solution. Currently, there are different solutions for data classifiers, such as the fuzzy C-means (FCM) [36], the K-means [37], the cluster-based density estimation (DBSCAN) [38], and more complex classifiers, such as support vector machines [39]. In each of these classifiers, initial values are required to make pertinent adjustments for different variations of the data entered and identify their behavior. Depending on the complexity of the problem, dimensionality reduction can be applied to the acquired data; in the particular case presented in this study, this reduction is necessary because the data will be processed on a mobile platform. Therefore, it is necessary that the classification techniques be simplified, reliable, efficient and available during the photo-id process. Systems capable of identifying animals in their habitats are an interesting challenge for image processing researchers due to the ability to achieve correct identification in the shortest possible execution time, guaranteeing intuitive and reliable development. Currently, the photo-id of the blue whale is done visually, so it can take several hours in the laboratory to identify, catalog and store the results obtained during the sightings. Therefore, it is important to consider the following points. Who uses the results?, the CICIMAR-IPN scientist. What do CICIMAR-IPN scientists use it for? After identifying and cataloging blue whales in the field for conservation purposes, where are the results used? In field expeditions off the coast of Baja California Sur, Mexico, identification has been developed as a passive data collector [40]. 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]. The current contributions of people on various technological platforms using the Internet or the different available social networks create the citizen sensor network [43] that is derived from the concept of citizen science, which has been used in various areas of knowledge for joint collaboration between the government, scientists and volunteers interested in the subject for conservation and ecology purposes. In this study, we present the results obtained during the research and implementation of PhotoId-Whale for the photo-id of blue whales for classification using mobile platform. We propose a classifier for the characteristic data obtained by the optimization of the SIFT presented in this research that reduces the coincidence of the characteristics. We illustrate its possible implementation on mobile platform and obtain greater portability in field studies. 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].
Median estimation classifier methodology
The methodology for the classification and identification of the dorsal fin of the blue whale is described in this section. A block diagram of the implemented stages is shown in Fig 1 . From this figure, it can be seen that the first step is to obtain an image from the blue whale database. The second step is the preprocessing of the image, which is divided into two substages: i) the selection of the region of interest (ROI) and ii) the segmentation of the image to extract the portion to be classified. The third step is the extraction of the characteristics by means of the SIFT and of the results obtained from the elimination of the redundant data through determining the variance and covariance of the final vectors. The refined vector is immediately entered into the data classifier to obtain the dorsal fin of the blue whale, which is then classified; the results are subsequently stored in the database for monitoring.
blue whale
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Also known as: Balaenoptera musculus, Sibbaldus musculus, sulfur-bottom, sulfur-bottom whale
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Last Updated: Oct 16, 2023 • Article History
Table of Contents
blue whale
Category: Animals & Nature
also called: sulfur-bottom whale . (Show more)
Related Topics: charismatic megafauna rorqual . (Show more)
blue whale, (Balaenoptera musculus), the most massive animal ever to have lived, a species of baleen whale that weighs approximately 150 tons and may attain a length of more than 30 metres (98 feet). The largest accurately measured blue whale was a 29.5-metre female that weighed 180 metric tons (nearly 200 short [U.S.] tons), but there are reports of 33-metre catches that may have reached 200 metric tons. The heart of one blue whale was recorded at nearly 700 kg (about 1,500 pounds).
The blue whale is a cetacean and is classified scientifically within the order Cetacea as a rorqual (family Balaenopteridae) related to the gray whale (family Eschrichtiidae) and the right whales (Balaenidae and Neobalaenidae) of the baleen whale suborder, Mysticeti.
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Blue whales are blue-gray in colour with lighter gray mottling in the form of large spots, which appear as if they were dabbed on with a huge paintbrush. The lower surfaces of the flippers are lighter gray or white in some instances. The blue whale has been called the sulfur-bottom whale because of the yellowish underside of some individuals that is reminiscent of the pale yellow colour of that chemical element; this coloration is imparted by certain algae (diatoms) living on the whale’s body. The blue whale has a wide head, a small dorsal fin located near the fluke, and 80–100 long grooves running lengthwise down the throat and chest. Its mouth contains up to 800 plates of short, wide, black baleen, or “whalebone,” with thick, coarse bristles used for catching food. Females are generally larger than males, and the largest animals live in the Southern Ocean around Antarctica.
The blue whale is found alone or in small groups in all oceans, but populations in the Southern Hemisphere are much larger. In the Northern Hemisphere, blue whales can be seen regularly in the Gulf of St. Lawrence and off the coasts of Monterey, California, and Baja California, Mexico. They spend the summer in polar waters, feeding on shrimplike crustaceans called krill. During a dive, the blue whale may engage in a series of turns and 360° rolls to locate prey and rapidly reorient its body to sweep up large concentrations of krill in a single open-mouthed lunge. A single adult blue may consume as much as eight tons of krill per day. In the winter blue whales move toward the Equator to breed. After a gestation of about 12 months, one calf about 8 metres (about 26 feet) long is born in temperate waters. While nursing, calves gain up to 90 kg (about 198 pounds) per day on the rich milk of their mothers. Young are weaned after seven to eight months, when they have reached a length of about 15 metres (about 49 feet).
Once the most important of the commercially hunted baleen whales, the blue whale was greatly reduced in numbers during the first half of the 20th century. In the 1930–31 season alone the worldwide kill of blue whales exceeded 29,000. The species has been protected from commercial whaling since the mid-1960s. Populations of blue whales appear to be recovering and are estimated worldwide at between 10,000 and 25,000 animals. However, the International Union for Conservation of Nature still lists the blue whale as an endangered species.
The Editors of Encyclopaedia Britannica This article was most recently revised and updated by John P. Rafferty.
