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Animal Wildlife Population Estimatio...

Foglio, Matteo.

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Animal Wildlife Population Estimation Using Social Media Images Collections.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Animal Wildlife Population Estimation Using Social Media Images Collections./
Author:	Foglio, Matteo.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	98 p.
Notes:	Source: Masters Abstracts International, Volume: 81-08.
Contained By:	Masters Abstracts International81-08.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27605211
ISBN:	9781687994301

Animal Wildlife Population Estimation Using Social Media Images Collections.
Foglio, Matteo.

Animal Wildlife Population Estimation Using Social Media Images Collections. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 98 p.

Source: Masters Abstracts International, Volume: 81-08.

Thesis (M.S.)--University of Illinois at Chicago, 2019.

This item must not be sold to any third party vendors.

We propose a method to estimate the number of animals of a given species using social media images collections. Traditional methods used to provide this estimate are expensive and time-consuming. Our solution aims at solving these issues by providing a species independent framework capable of dealing with social media biases. In fact, while biologists are usually in charge of the data collection process, when using social media as a source of data, we have to deal with biased datasets. A photographer may choose to share only its best pictures, hiding us information useful to estimate the number of animals. Previous works have shown that there is indeed a bias related to individual pictures. In our approach, we extend these researches by studying the bias at the level of images collections.We propose an approach based on two steps. The first step consists in the use of a regression model to estimate the number of animals photographed by a social media user, but not shared on the social media platform. The second step is the use of traditional wildlife estimator to predict the number of animals of a given species. This traditional model will be fed with data predicted by the regression model when applying it on several images collections retrieved from social media.

ISBN: 9781687994301Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Wildlife

Animal Wildlife Population Estimation Using Social Media Images Collections.
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100 1 $a Foglio, Matteo. $3 3550342
245 1 0 $a Animal Wildlife Population Estimation Using Social Media Images Collections.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 98 p.
500 $a Source: Masters Abstracts International, Volume: 81-08.
500 $a Advisor: Berger-Wolf, Tanya Y.
502 $a Thesis (M.S.)--University of Illinois at Chicago, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a We propose a method to estimate the number of animals of a given species using social media images collections. Traditional methods used to provide this estimate are expensive and time-consuming. Our solution aims at solving these issues by providing a species independent framework capable of dealing with social media biases. In fact, while biologists are usually in charge of the data collection process, when using social media as a source of data, we have to deal with biased datasets. A photographer may choose to share only its best pictures, hiding us information useful to estimate the number of animals. Previous works have shown that there is indeed a bias related to individual pictures. In our approach, we extend these researches by studying the bias at the level of images collections.We propose an approach based on two steps. The first step consists in the use of a regression model to estimate the number of animals photographed by a social media user, but not shared on the social media platform. The second step is the use of traditional wildlife estimator to predict the number of animals of a given species. This traditional model will be fed with data predicted by the regression model when applying it on several images collections retrieved from social media.
590 $a School code: 0799.
650 4 $a Computer science. $3 523869
650 4 $a Web studies. $3 2122754
650 4 $a Artificial intelligence. $3 516317
650 4 $a Wildlife management. $3 571816
650 4 $a Wildlife conservation. $2 fast $3 542165
653 $a Wildlife
653 $a Population
653 $a Estimation
653 $a Social media
653 $a Images
653 $a Collections
653 $a Machine learning
690 $a 0984
690 $a 0800
690 $a 0646
690 $a 0286
690 $a 0284
710 2 $a University of Illinois at Chicago. $b Computer Science. $3 2094830
773 0 $t Masters Abstracts International $g 81-08.
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791 $a M.S.
792 $a 2019
793 $a English
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