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Bird Species Classification from an Image Using VGG-16 Network

Published: 27 July 2019 Publication History

Abstract

Birds are an integral part of any environment and they are of the utmost importance to nature. Considering this, it is clear how necessary it is to be able to identify birds in the wilderness. This paper proposes a Machine Learning approach to identify Bangladeshi birds according to their species. We used VGG-16 network as our model to extract the features from bird images. In order to perform the classification, we used a data set that contains pictures of different bird species of Bangladesh which were used as they are, without any annotation. We then used various classification methods, where each method gave us different results. However, compared to other classification methods such as Random Forest and K-Nearest Neighbor (KNN), Support Vector Machine (SVM) gave us the maximum accuracy of 89%.

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    cover image ACM Other conferences
    ICCCM '19: Proceedings of the 7th International Conference on Computer and Communications Management
    July 2019
    260 pages
    ISBN:9781450371957
    DOI:10.1145/3348445
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 27 July 2019

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    Author Tags

    1. Bird's species classification
    2. K-Nearest Neighbors
    3. Random Forest
    4. Support Vector Machine
    5. VGG-16

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    Cited By

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    • (2024)A Birds Species Detection Utilizing an Effective Hybrid Model2024 21st International Multi-Conference on Systems, Signals & Devices (SSD)10.1109/SSD61670.2024.10549480(705-710)Online publication date: 22-Apr-2024
    • (2024)Feathered Precision: AvianVision - A Hybrid CNN-Random Forest Approach for Accurate Classification of Sparrow Species2024 11th International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN60856.2024.10511888(215-220)Online publication date: 21-Mar-2024
    • (2024)Recognition of Bird Species of Gaoligong Mountain using Transfer Learning Based on VGG-162024 4th International Conference on Neural Networks, Information and Communication (NNICE)10.1109/NNICE61279.2024.10498802(643-647)Online publication date: 19-Jan-2024
    • (2024)EBSDNET - Endanger Bird Species Detection Net using ResNet502024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON)10.1109/NMITCON62075.2024.10698906(1-6)Online publication date: 9-Aug-2024
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    • (2024)Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE59897.2024.10467695(1-10)Online publication date: 24-Jan-2024
    • (2024)VGG16: Offline handwritten devanagari word recognition using transfer learningMultimedia Tools and Applications10.1007/s11042-024-18394-783:29(72561-72594)Online publication date: 10-Feb-2024
    • (2024)Advancing Bird Classification: Harnessing PSA-DenseNet for Call-Based RecognitionProceedings of Workshop on Interdisciplinary Sciences 202310.1007/978-981-97-7850-8_6(81-89)Online publication date: 21-Oct-2024
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