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Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

dc.contributor.authorZhaojun Wang
dc.contributor.authorJiangning Wang
dc.contributor.authorCongtian Lin
dc.contributor.authorYan Han
dc.contributor.authorZhaosheng Wang
dc.contributor.authorLiqiang Ji
dc.contributor.otherKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
dc.contributor.otherKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
dc.contributor.otherKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
dc.contributor.otherKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
dc.contributor.otherNational Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
dc.contributor.otherKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
dc.date.accessioned2021-04-28T00:05:56Z
dc.date.available2025-10-02T04:37:46Z
dc.date.issued01-04-2021
dc.identifier.issn-
dc.identifier.urihttps://www.mdpi.com/2076-2615/11/5/1263
dc.description.abstractWith the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.
dc.format-
dc.language.isoEN
dc.publisherMDPI AG
dc.relation.uri['https://www.elsevier.com/journals/annals-of-hepatology/1665-2681/guide-for-authors', 'https://www.journals.elsevier.com/annals-of-hepatology', 'https://www.elsevier.com/authors/open-access/choice#waivers']
dc.rights['CC BY', 'CC BY-NC-ND']
dc.subject['alcoholic liver disease', 'autoimmune hepatitis', 'biliary diseases', 'drug-induced liver injury', 'genetic liver diseases', 'viral hepatitis', 'Specialties of internal medicine', 'RC581-951']
dc.subject.lccVeterinary medicine
dc.titleIdentifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks
dc.typeArticle
dc.description.keywordsbird images
dc.description.keywordsdeep convolutional neural networks
dc.description.keywordshabitat elements
dc.description.pages-
dc.description.doi10.3390/ani11051263
dc.title.journalAnimals
dc.identifier.e-issn2076-2615
dc.identifier.oaioai:doaj.org/journal:79d9d5d7ba174acd994a5a44b5240051
dc.journal.infoVolume 11, Issue 5


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