{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:07:38Z","timestamp":1760234858402,"version":"build-2065373602"},"reference-count":81,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T00:00:00Z","timestamp":1623974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773325, 61806173"],"award-info":[{"award-number":["61773325, 61806173"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Joint Funds of 5th Round of Health and Education Research Program of Fujian Province","award":["2019-WJ-41"],"award-info":[{"award-number":["2019-WJ-41"]}]},{"name":"Joint Funds of Scientific and Technological Innovation Program of Fujian Province","award":["2017Y9059"],"award-info":[{"award-number":["2017Y9059"]}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2019J05123"],"award-info":[{"award-number":["2019J05123"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fine-grained image classification is a hot topic that has been widely studied recently. Many fine-grained image classification methods ignore misclassification information, which is important to improve classification accuracy. To make use of misclassification information, in this paper, we propose a novel fine-grained image classification method by exploring the misclassification information (FGMI) of prelearned models. For each class, we harvest the confusion information from several prelearned fine-grained image classification models. For one particular class, we select a number of classes which are likely to be misclassified with this class. The images of selected classes are then used to train classifiers. In this way, we can reduce the influence of irrelevant images to some extent. We use the misclassification information for all the classes by training a number of confusion classifiers. The outputs of these trained classifiers are combined to represent images and produce classifications. To evaluate the effectiveness of the proposed FGMI method, we conduct fine-grained classification experiments on several public image datasets. Experimental results prove the usefulness of the proposed method.<\/jats:p>","DOI":"10.3390\/s21124176","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T04:10:47Z","timestamp":1623989447000},"page":"4176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploring Misclassification Information for Fine-Grained Image Classification"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5901-0778","authenticated-orcid":false,"given":"Da-Han","family":"Wang","sequence":"first","affiliation":[{"name":"Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen 361024, China"},{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen 361024, China"},{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianmin","family":"Li","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen 361024, China"},{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Wu","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen 361024, China"},{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunzhi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen 361024, China"},{"name":"School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Nilsback, M., and Zisserman, A. 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