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Analysis of Learning Techniques: Bird Species Classification

by Pranav Kumar Rai, Sourvi Chaturvedi, Sandhya Katiyar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 17
Year of Publication: 2019
Authors: Pranav Kumar Rai, Sourvi Chaturvedi, Sandhya Katiyar
10.5120/ijca2019918969

Pranav Kumar Rai, Sourvi Chaturvedi, Sandhya Katiyar . Analysis of Learning Techniques: Bird Species Classification. International Journal of Computer Applications. 178, 17 ( Jun 2019), 12-16. DOI=10.5120/ijca2019918969

@article{ 10.5120/ijca2019918969,
author = { Pranav Kumar Rai, Sourvi Chaturvedi, Sandhya Katiyar },
title = { Analysis of Learning Techniques: Bird Species Classification },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2019 },
volume = { 178 },
number = { 17 },
month = { Jun },
year = { 2019 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number17/30625-2019918969/ },
doi = { 10.5120/ijca2019918969 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:50:40.018755+05:30
%A Pranav Kumar Rai
%A Sourvi Chaturvedi
%A Sandhya Katiyar
%T Analysis of Learning Techniques: Bird Species Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 17
%P 12-16
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Identification of bird species is a difficult problem that pushes the limits of the visual abilities for both humans and computers. Although different bird species share the same basic set of parts, different bird species can vary dramatically in shape and appearance[5]. Sometimes professional bird watchers disagree on the species given an image of a bird. Intra-class variance is high due to variation in lighting and background and extreme variation in pose (e.g., flying birds, swimming birds, and perched birds that are partially occluded by branches). In this paper, a simple image recognition classifier has been created. This image recognition tool classifies various species of birds. An application CNN has been used in order to extract features from the input image. Convolution preserves the spatial relationship between pixels by learning image features using small squares of input data. Then application of some of the supervised and unsupervised algorithms is used to check and compare their accuracies against each other. The aim is to find which learning algorithm’s accuracy is best in predicting a particular specie of bird given any image of it.

References
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Index Terms

Computer Science
Information Sciences

Keywords

CNN Image classifier accuracy bird species.

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