{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:57:35Z","timestamp":1790697455362,"version":"4.1.0"},"reference-count":37,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T00:00:00Z","timestamp":1554076800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,7,9]]},"abstract":"<jats:p>\u00a0Fine-Grained ship classification is quite challenging because the visual differences between the subcategories are small. Due to the large intra-class similarity, it is very difficult to classify the ship objects without bounding box\/part annotations. In this paper, we propose a model that combines multiple deep CNN features and use fusion strategies to explore of multi-scale features relationship. Because different levels\/depths CNN features have different properties, so we combine multiple low-level local CNN features with high-level global CNN feature for object classification. The model shows a good way of tailoring pre-trained CNN models to fine-grained ship classification, which have lower cost in computation and storage compared with some state-of-the-art CNN methods and achieves the significant classification performances in FGVC-Aircraft and Stanford Cars datasets.<\/jats:p>","DOI":"10.3233\/jifs-179071","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T13:46:24Z","timestamp":1554212784000},"page":"125-135","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["Multi-feature fusion of convolutional neural networks for Fine-Grained ship classification"],"prefix":"10.1177","volume":"37","author":[{"given":"Sizhe","family":"Huang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, China"},{"name":"Wuhan Digital Engineering Research Institute, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huosheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Wuhan Digital Engineering Research Institute, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuezhi","family":"Xia","sequence":"additional","affiliation":[{"name":"Wuhan Digital Engineering Research Institute, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuhao","family":"Zou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,4]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2010.512310"},{"key":"e_1_3_1_3_2","first-page":"684","article-title":"Pattern recognition for ship based on Bayesian networks","volume":"4","author":"Wang Q.J.","year":"2007","unstructured":"WangQ.J., ChenD.Q. and ChenD.Q., Pattern recognition for ship based on Bayesian networks, International Conference on Fuzzy Systems and Knowledge Discovery4 (2007), 684\u2013688.","journal-title":"International Conference on Fuzzy Systems and Knowledge Discovery"},{"key":"e_1_3_1_4_2","first-page":"514","article-title":"Ship detection and recognition in high-resolution satellite images","volume":"4","author":"Antelo J.","year":"2009","unstructured":"AnteloJ., AmbrosioG., GonzalezJ. and GalindoC., Ship detection and recognition in high-resolution satellite images, Geoscience and Remote Sensing Symposium4 (2009), 514\u2013517.","journal-title":"Geoscience and Remote Sensing Symposium"},{"key":"e_1_3_1_5_2","first-page":"509","article-title":"Ship recognition based on improved forwards-backwards algorithm","volume":"5","author":"Chen H.","year":"2009","unstructured":"ChenH. and GaoX., Ship recognition based on improved forwards-backwards algorithm, International Conference on Fuzzy Systems and Knowledge Discovery5 (2009), 509\u2013513.","journal-title":"International Conference on Fuzzy Systems and Knowledge Discovery"},{"key":"e_1_3_1_6_2","first-page":"1","article-title":"Ship detection in images obtained from the unmanned aerial vehicle (uav)","volume":"9","author":"Dolgopolov A.V.","year":"2017","unstructured":"DolgopolovA.V., KazantsevP.A., BezuhliyN.N., DolgopolovA.V., KazantsevP.A. and BezuhliyN.N., Ship detection in images obtained from the unmanned aerial vehicle (uav), Indian Journal of Science & Technology9 (2017), 1\u20137.","journal-title":"Indian Journal of Science & Technology"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2335751"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","unstructured":"LiuY. CuiH.Y. KuangZ. and LiG.Q. Ship detection and classification on optical remote sensing images using deep learning 12 (2017) 05012.","DOI":"10.1051\/itmconf\/20171205012"},{"key":"e_1_3_1_9_2","article-title":"Bird species categorization using pose normalized deep convolutional nets","author":"Branson S.","year":"2014","unstructured":"BransonS., HornG.V., BelongieS.J. and PeronaP., Bird species categorization using pose normalized deep convolutional nets, In BMVC, 2014.","journal-title":"In BMVC"},{"key":"e_1_3_1_10_2","article-title":"Learning deep representations of fine-grained visual descriptions","author":"Reed S.E.","year":"2016","unstructured":"ReedS.E., AkataZ., SchieleB. and Lee.H., Learning deep representations of fine-grained visual descriptions, In CVPR, 2016.","journal-title":"In CVPR"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"LinY.-L. MorariuV.I. HsuW. DavisL.S. Jointly optimizing 3d model fitting and fine-grained classification In European Conference on Computer Vision (ECCV) Springer 2014 pp. 466\u2013480.","DOI":"10.1007\/978-3-319-10593-2_31"},{"key":"e_1_3_1_12_2","first-page":"1449","article-title":"Bilinear CNN Models for Fine-Grained Visual Recognition, pp.\u00a0","author":"Lin T.Y.","year":"2016","unstructured":"LinT.Y., RoychowdhuryA. and MajiS., Bilinear CNN Models for Fine-Grained Visual Recognition, pp.\u00a0, IEEE International Conference on Computer Vision (2016), 1449\u20131457.","journal-title":"IEEE International Conference on Computer Vision"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2648498"},{"key":"e_1_3_1_14_2","first-page":"1641","article-title":"Hierarchical part matching for fine-grained visual categorization, pp.\u00a0","author":"Xie L.","year":"2013","unstructured":"XieL., TianQ., HongR., YanS. and ZhangB., Hierarchical part matching for fine-grained visual categorization, pp.\u00a0, IEEE International Conference on Computer Vision (2013), 1641\u20131648.","journal-title":"IEEE International Conference on Computer Vision"},{"key":"e_1_3_1_15_2","first-page":"161","article-title":"Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance","volume":"23","author":"Farrell R.","year":"2011","unstructured":"FarrellR., OzaO., ZhangN., MorariuV.I., DarrellT. and DavisL.S., Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance, IEEE International Conference on Computer Vision23 (2011), 161\u2013168.","journal-title":"IEEE International Conference on Computer Vision"},{"key":"e_1_3_1_16_2","first-page":"2740","article-title":"Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition","author":"Yan Z.","year":"2014","unstructured":"YanZ., ZhangH., PiramuthuR., JagadeeshV., DecosteD. and DiW., Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition, IEEE International Conference on Computer Vision, 2014, pp. 2740\u20132748.","journal-title":"IEEE International Conference on Computer Vision"},{"key":"e_1_3_1_17_2","first-page":"5546","article-title":"Fine-grained recognition without part annotations","author":"Krause J.","year":"2015","unstructured":"KrauseJ., JinH., YangJ. and LiF.F., Fine-grained recognition without part annotations, IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5546\u20135555.","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_18_2","first-page":"6497","article-title":"Fine-grained recognition as HSnet search for informative image parts","author":"Lam M.","year":"2017","unstructured":"LamM., MahasseniB. and TodorovicS., Fine-grained recognition as HSnet search for informative image parts, IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 6497\u20136506.","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2016.2602060"},{"key":"e_1_3_1_20_2","first-page":"26","article-title":"Learning features and parts for fine-grained recognition","author":"Krause J.","year":"2014","unstructured":"KrauseJ., GebruT., DengJ., LiL.J. and LiF.F., Learning features and parts for fine-grained recognition, International Conference on Pattern Recognition, 2014, pp. 26\u201333.","journal-title":"International Conference on Pattern Recognition"},{"key":"e_1_3_1_21_2","first-page":"4476","article-title":"Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition","author":"Fu J.","year":"2017","unstructured":"FuJ., ZhengH. and MeiT., Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition, IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4476\u20134484.","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_22_2","first-page":"4232","article-title":"Two-stream contextualized CNN for fine-grained image classification","author":"Liu J.","year":"2016","unstructured":"LiuJ., GaoC., MengD. and ZuoW., Two-stream contextualized CNN for fine-grained image classification, Thirtieth AAAI Conference on Artificial Intelligence, 2016, pp. 4232\u20134233.","journal-title":"Thirtieth AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","unstructured":"WangA. CaiJ. LuJ. and ChamT.J. Modality and Component Aware Feature Fusion for RGB-D Scene Classification Computer Vision and Pattern Recognition 2016 pp. 5995\u20136004.","DOI":"10.1109\/CVPR.2016.645"},{"key":"e_1_3_1_24_2","first-page":"2983","article-title":"Joint fine-tuning in deep neural networks for facial expression recognition","author":"Jung H.","year":"2016","unstructured":"JungH., LeeS., YimJ., ParkS. and KimJ., Joint fine-tuning in deep neural networks for facial expression recognition, IEEE International Conference on Computer Vision, 2016, pp. 2983\u20132991.","journal-title":"IEEE International Conference on Computer Vision"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654931"},{"key":"e_1_3_1_26_2","first-page":"457","article-title":"RAPID: Rating pictorial aesthetics using deep learning","volume":"17","author":"Yang J.","year":"2014","unstructured":"YangJ., YangJ., YangJ., YangJ. and WangJ.Z., RAPID: Rating pictorial aesthetics using deep learning, ACM International Conference on Multimedia17 (2014), 457\u2013466.","journal-title":"ACM International Conference on Multimedia"},{"key":"e_1_3_1_27_2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky A.","year":"2012","unstructured":"KrizhevskyA., SutskeverI. and HintonG.E., ImageNet classification with deep convolutional neural networks, International Conference on Neural Information Processing Systems60 (2012), 1097\u20131105.","journal-title":"International Conference on Neural Information Processing Systems"},{"key":"e_1_3_1_28_2","first-page":"2921","article-title":"Learning deep features for discriminative localization","author":"Zhou B.","year":"2016","unstructured":"ZhouB., KhoslaA., LapedrizaA., OlivaA. and TorralbaA., Learning deep features for discriminative localization, IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2921\u20132929.","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_29_2","first-page":"3119","article-title":"Visual tracking with fully convolutional networks","author":"Wang L.","year":"2016","unstructured":"WangL., OuyangW., WangX. and LuH., Visual tracking with fully convolutional networks, IEEE International Conference on Computer Vision, 2016, pp. 3119\u20133127.","journal-title":"IEEE International Conference on Computer Vision"},{"key":"e_1_3_1_30_2","first-page":"1717","article-title":"Learning and transferring mid-level image representations using convolutional neural networks","author":"Oquab M.","year":"2014","unstructured":"OquabM., BottouL., LaptevI. and SivicJ., Learning and transferring mid-level image representations using convolutional neural networks, IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1717\u20131724.","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_31_2","unstructured":"MajiS. RahtuE. KannalaJ. BlaschkoM. and VedaldiA. Fine-grained visual classification of aircraft arXiv preprint arXiv:1306.5151 2013. 5 6."},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","unstructured":"JanKrause MichaelStark JiaDeng and LiFei-Fei 3d object representations for fine-grained categorization in Computer VisionWorkshops (ICCVW) 2013 pp. 554\u2013561.","DOI":"10.1109\/ICCVW.2013.77"},{"key":"e_1_3_1_33_2","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan K.","year":"2014","unstructured":"SimonyanK. and ZissermanA., Very deep convolutional networks for large-scale image recognition, Computer Science, 2014.","journal-title":"Computer Science"},{"key":"e_1_3_1_34_2","unstructured":"SzegedyC. LiuW. JiaY. SermanetP. ReedS. AnguelovD. ErhanD. VanhouckeV. and RabinovichA. Going deeper with convolutions. arXiv preprint arXiv:1409 4842 2014. 3."},{"key":"e_1_3_1_35_2","first-page":"321","article-title":"Symbiotic segmentation and part localization for fine-grained categorization","volume":"163","author":"Chai Y.","year":"2013","unstructured":"ChaiY., LempitskyV. and ZissermanA., Symbiotic segmentation and part localization for fine-grained categorization, IEEE International Conference on Computer Vision163 (2013), 321\u2013328.","journal-title":"IEEE International Conference on Computer Vision"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2014.06.011"},{"key":"e_1_3_1_37_2","unstructured":"LiuX. XiaT. WangJ. and LinY. Fully convolutional attention localization networks: Efficient attention localization for fine-grained recognition CoRR abs\/1603.06765 2016."},{"key":"e_1_3_1_38_2","first-page":"82","article-title":"Convolutional channel features","author":"Yang B.","year":"2015","unstructured":"YangB., YanJ., LeiZ. and LiS.Z., Convolutional channel features, IEEE International Conference on Computer Vision, 2015, pp. 82\u201390.","journal-title":"IEEE International Conference on Computer Vision"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179071","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-179071","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179071","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:38:22Z","timestamp":1777455502000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-179071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,7,9]]}},"alternative-id":["10.3233\/JIFS-179071"],"URL":"https:\/\/doi.org\/10.3233\/jifs-179071","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4]]}}}