{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:59Z","timestamp":1740122819198,"version":"3.37.3"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"21-22","license":[{"start":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T00:00:00Z","timestamp":1543363200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T00:00:00Z","timestamp":1543363200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603289"],"award-info":[{"award-number":["61603289"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2016M602823"],"award-info":[{"award-number":["2016M602823"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["xjj2017118"],"award-info":[{"award-number":["xjj2017118"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s11042-018-6915-3","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T06:55:10Z","timestamp":1543388110000},"page":"14465-14489","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised semantic-based convolutional features aggregation for image retrieval"],"prefix":"10.1007","volume":"79","author":[{"given":"Xinsheng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanmin","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihua","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,28]]},"reference":[{"key":"6915_CR1","doi-asserted-by":"crossref","unstructured":"Azizpour H, Razavian AS, Sullivan J, Maki A, Carlsson S (2015) From generic to specific deep representations for visual recognition. In: Computer vision and pattern recognition workshops. pp 36\u201345","DOI":"10.1109\/CVPRW.2015.7301270"},{"key":"6915_CR2","unstructured":"Babenko A, Lempitsky V (2015) Aggregating deep convolutional features for image retrieval. Computer Science"},{"key":"6915_CR3","first-page":"584","volume":"8689","author":"A Babenko","year":"2014","unstructured":"Babenko A, Slesarev A, Chigorin A, Lempitsky V (2014) Neural Codes for Image Retrieval 8689:584\u2013599","journal-title":"Neural Codes for Image Retrieval"},{"issue":"3","key":"6915_CR4","doi-asserted-by":"publisher","first-page":"737","DOI":"10.3390\/s18030737","volume":"18","author":"X Cao","year":"2018","unstructured":"Cao X, Wang P, Meng C, Bai X, Gong G, Liu M, Qi J (2018) Region based CNN for foreign object debris detection on airfield pavement. Sensors 18(3):737","journal-title":"Sensors"},{"key":"6915_CR5","doi-asserted-by":"crossref","unstructured":"Chen Z, Kuang Z, Wong KYK, Zhang W (2017) Aggregated deep feature from activation clusters for particular object retrieval. In: Thematic workshops of ACM multimedia. pp 44\u201351","DOI":"10.1145\/3126686.3126696"},{"key":"6915_CR6","unstructured":"Chu WT, Wu YL (2018) Image style classification based on learnt deep correlation features. IEEE Trans Multimed (99):1\u20131"},{"key":"6915_CR7","doi-asserted-by":"crossref","unstructured":"Chum O, Philbin J, Sivic J, Isard M, Zisserman A (2007) Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval. 1\u20138","DOI":"10.1109\/ICCV.2007.4408891"},{"key":"6915_CR8","doi-asserted-by":"crossref","unstructured":"Do TT, Hoang T, Tan DKL, Cheung NM (2018) From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval","DOI":"10.1145\/3123266.3123417"},{"issue":"9","key":"6915_CR9","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1109\/TMM.2017.2729019","volume":"19","author":"L Gao","year":"2017","unstructured":"Gao L, Guo Z, Zhang H, Xu X, Shen HT (2017) Video captioning with attention-based LSTM and semantic consistency. IEEE Trans Multimed 19(9):2045\u20132055","journal-title":"IEEE Trans Multimed"},{"key":"6915_CR10","doi-asserted-by":"crossref","unstructured":"Gong Y, Wang L, Guo R, Lazebnik S (2014) Multi-scale Orderless Pooling of Deep Convolutional Activation Features. 8695:392\u2013407","DOI":"10.1007\/978-3-319-10584-0_26"},{"key":"6915_CR11","doi-asserted-by":"crossref","unstructured":"Gordo A, Almaz\u00e1n J, Revaud J, Larlus D (2016) Deep image retrieval: learning global representations for image search. In: European conference on computer vision. pp 241\u2013257","DOI":"10.1007\/978-3-319-46466-4_15"},{"key":"6915_CR12","unstructured":"Gordo A, Almaz\u00e1n J, Revaud J, Larlus D (2016) End-to-end learning of deep visual representations for image retrieval. Int J Comput Vis:1\u201318"},{"key":"6915_CR13","doi-asserted-by":"crossref","unstructured":"He L, Xu X, Lu H, Yang Y, Shen F, Shen HT (2017) Unsupervised cross-modal retrieval through adversarial learning. IEEE Int Conf Multimed Expo: 1153\u20131158","DOI":"10.1109\/ICME.2017.8019549"},{"key":"6915_CR14","doi-asserted-by":"crossref","unstructured":"J\u00e9gou H, Chum O (2012) Negative evidences and co-occurences in image retrieval: the benefit of PCA and whitening. Eur Conf Comput Vision: 774\u2013787","DOI":"10.1007\/978-3-642-33709-3_55"},{"key":"6915_CR15","unstructured":"J\u00e9gou H, Zisserman A (2014) Triangulation Embedding and Democratic aggregation for image search. In: Computer vision and pattern recognition. pp 3310\u20133317"},{"key":"6915_CR16","doi-asserted-by":"crossref","unstructured":"Jegou H, Douze M, Schmid C (2009) On the burstiness of visual elements. Computer Vision Pattern Recogn 2009. CVPR 2009. IEEE Conf: 1169\u20131176","DOI":"10.1109\/CVPR.2009.5206609"},{"key":"6915_CR17","unstructured":"Jian X, Chunheng W, Chengzuo Q, Cunzhao S, Baihua X (2018) Unsupervised Semantic-based Aggregation of Deep Convolutional Features. arXiv:10"},{"key":"6915_CR18","doi-asserted-by":"crossref","unstructured":"Kalantidis Y, Mellina C, Osindero S (2016) Cross-dimensional weighting for aggregated deep convolutional features. In: European conference on computer vision. 685\u2013701","DOI":"10.1007\/978-3-319-46604-0_48"},{"key":"6915_CR19","doi-asserted-by":"crossref","unstructured":"Kim DS, Arsalan M, Park KR (2018) Convolutional neural network-based shadow detection in images using visible light camera sensor. Sensors 18 (4)","DOI":"10.3390\/s18040960"},{"key":"6915_CR20","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Int Conf Neural Inform Process Syst: 1097\u20131105"},{"issue":"2","key":"6915_CR21","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe DG (2004) Distinctive image features from scale-invariant Keypoints. Int J Comput Vis 60(2):91\u2013110","journal-title":"Int J Comput Vis"},{"issue":"2","key":"6915_CR22","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1007\/s11036-017-0932-8","volume":"23","author":"H Lu","year":"2017","unstructured":"Lu H, Li Y, Chen M, Kim H, Serikawa S (2017) Brain intelligence: go beyond artificial intelligence. Mobile Netw Appl 23(2):368\u2013375","journal-title":"Mobile Netw Appl"},{"key":"6915_CR23","unstructured":"Lu H, Li Y, Mu S, Wang D, Kim H, Serikawa S (2017) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Int Things J PP (99):1\u20131"},{"key":"6915_CR24","unstructured":"Lu H, Li Y, Uemura T, Ge Z, Xu X, He L, Serikawa S, Kim H (2017) FDCNet: filtering deep convolutional network for marine organism classification. Multimed Tools Appl (2):1\u201314"},{"key":"6915_CR25","doi-asserted-by":"crossref","unstructured":"Lu H, Li B, Zhu J, Li Y, Li Y, Xu X, He L, Li X, Li J, Serikawa S (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurr Comput Pract Exper 29 (6)","DOI":"10.1002\/cpe.3927"},{"key":"6915_CR26","doi-asserted-by":"crossref","unstructured":"Lu H, Li Y, Uemura T, Kim H, Serikawa S (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Futur Gener Comput Syst 82","DOI":"10.1016\/j.future.2018.01.001"},{"key":"6915_CR27","unstructured":"Mao XJ, Shen C, Yang YB (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections"},{"key":"6915_CR28","unstructured":"Pang S, Ma J, Zhu J, Xue J, Tian Q Improving object retrieval quality by integration of similarity propagation and query expansion. IEEE Trans Multimed (99):1\u20131"},{"key":"6915_CR29","doi-asserted-by":"crossref","unstructured":"Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2007) Object retrieval with large vocabularies and fast spatial matching. In: Computer vision and pattern recognition, 2007. CVPR 2007. IEEE conference on. pp 1\u20138","DOI":"10.1109\/CVPR.2007.383172"},{"key":"6915_CR30","doi-asserted-by":"crossref","unstructured":"Philbin J, Chum O, Isard M, Sivic J (2008) Lost in quantization: improving particular object retrieval in large scale image databases. In: Computer vision and pattern recognition, 2008. CVPR 2008. IEEE conference on. pp 1\u20138","DOI":"10.1109\/CVPR.2008.4587635"},{"key":"6915_CR31","doi-asserted-by":"crossref","unstructured":"Radenovi\u0107 F, Tolias G, Chum O CNN (2016) Image retrieval learns from BoW: unsupervised fine-tuning with hard examples. In: European conference on computer vision. 3\u201320","DOI":"10.1007\/978-3-319-46448-0_1"},{"issue":"10","key":"6915_CR32","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.3390\/s17102421","volume":"17","author":"L Ran","year":"2017","unstructured":"Ran L, Zhang Y, Wei W, Zhang Q (2017) A hyperspectral image classification framework with spatial pixel pair features. Sensors 17(10):2421","journal-title":"Sensors"},{"key":"6915_CR33","doi-asserted-by":"crossref","unstructured":"Razavian AS, Azizpour H, Sullivan J, Carlsson S CNN (2014) Features off-the-shelf: an astounding baseline for recognition. In: IEEE conference on computer vision and pattern recognition workshops. 512\u2013519","DOI":"10.1109\/CVPRW.2014.131"},{"key":"6915_CR34","unstructured":"Razavian AS, Sullivan J, Maki A, Carlsson S (2014) A baseline for visual instance retrieval with deep convolutional networks. Computer Science"},{"issue":"6","key":"6915_CR35","first-page":"1137","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE transactions on Pattern Analysis & Machine. Intelligence 39(6):1137\u20131149","journal-title":"Intelligence"},{"issue":"3","key":"6915_CR36","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"issue":"3","key":"6915_CR37","first-page":"864","volume":"109","author":"BL Scott","year":"2018","unstructured":"Scott BL, Hardesty LH (2018) Method and apparatus for speech recognition. J Acoust Soc Am 109(3):864","journal-title":"J Acoust Soc Am"},{"key":"6915_CR38","doi-asserted-by":"crossref","unstructured":"Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Pergamon press, Inc","DOI":"10.1016\/j.compeleceng.2013.10.016"},{"issue":"4","key":"6915_CR39","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640\u2013651","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6915_CR40","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Computer Science"},{"key":"6915_CR41","unstructured":"Tolias G, Sicre R, J\u00e9gou H (2015) Particular object retrieval with integral max-pooling of CNN activations. Computer Science"},{"key":"6915_CR42","doi-asserted-by":"crossref","unstructured":"Tollari S, Detyniecki M, Marsala C, Fakeri-Tabrizi A, Amini MR, Gallinari P (2009) Exploiting visual concepts to improve text-based image retrieval. Eur Conf Ir Res Adv Inform Retriev: 701\u2013705","DOI":"10.1007\/978-3-642-00958-7_70"},{"key":"6915_CR43","unstructured":"Tuan H, Thanh-Toan D, Dang-Khoa Le T, Ngai-Man C (2017) Selective deep convolutional features for image retrieval arXiv:9 pp.-9 pp"},{"issue":"3","key":"6915_CR44","doi-asserted-by":"publisher","first-page":"769","DOI":"10.3390\/s18030769","volume":"18","author":"L Wang","year":"2018","unstructured":"Wang L, Xu X, Dong H, Gui R, Pu F (2018) Multi-pixel simultaneous classification of PolSAR image using convolutional neural networks. Sensors 18(3):769","journal-title":"Sensors"},{"key":"6915_CR45","unstructured":"Wang X, Pang S, Zhu J, Wang J, Wang L (2018) An efficient aggregation method of convolutional features for image retrieval. In: International symposium on artificial intelligence and robotics, Nanjing, China"},{"key":"6915_CR46","doi-asserted-by":"crossref","unstructured":"Wang J, Zhu J, Pang S, Li Z, Li Y, Qian X (2018) Adaptive Co-weighting Deep Convolutional Features For Object Retrieval","DOI":"10.1109\/ICME.2018.8486610"},{"key":"6915_CR47","first-page":"1","volume":"99","author":"XS Wei","year":"2016","unstructured":"Wei XS, Luo JH, Wu J, Zhou ZH (2016) Selective convolutional descriptor aggregation for fine-grained image retrieval. IEEE Trans Image Proc 99:1\u20131","journal-title":"IEEE Trans Image Proc"},{"key":"6915_CR48","unstructured":"Xiu-Shen W, Jian-Hao L, Jianxin W (2016) Selective convolutional descriptor aggregation for fine-grained image retrieval. arXiv:16 pp.-16 pp."},{"key":"6915_CR49","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neucom.2015.11.133","volume":"213","author":"X Xu","year":"2016","unstructured":"Xu X, He L, Shimada A, Taniguchi RI, Lu H (2016) Learning unified binary codes for cross-modal retrieval via latent semantic hashing. Neurocomputing 213:191\u2013203","journal-title":"Neurocomputing"},{"key":"6915_CR50","doi-asserted-by":"crossref","unstructured":"Xu X, Shen F, Yang Y, Shen HT, Li X (2017) Learning discriminative binary codes for large-scale cross-modal retrieval. IEEE Trans Image Process (99):1\u20131","DOI":"10.1109\/TIP.2017.2676345"},{"key":"6915_CR51","doi-asserted-by":"crossref","unstructured":"Xu J, Shi C, Qi C, Wang C, Xiao B (2017) Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval","DOI":"10.1609\/aaai.v32i1.12231"},{"key":"6915_CR52","unstructured":"Xu X, He L, Lu H, Gao L, Ji Y (2018) Deep adversarial metric learning for cross-modal retrieval. World Wide web-internet & web Inf Syst:1\u201316"},{"key":"6915_CR53","unstructured":"Yandex AB, Lempitsky V (2016) Aggregating local deep features for image retrieval. In: IEEE international conference on computer vision. 1269\u20131277"},{"key":"6915_CR54","doi-asserted-by":"crossref","unstructured":"Yang J, She D, Sun M, Cheng MM, Rosin P, Wang L (2018) Visual sentiment prediction based on automatic discovery of affective regions. IEEE Trans Multimed (99):1\u20131","DOI":"10.1109\/TMM.2018.2803520"},{"key":"6915_CR55","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Fergus R (2013) Visualizing and Understanding Convolutional Networks 8689:818\u2013833","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"6915_CR56","doi-asserted-by":"crossref","unstructured":"Zhang X, Xiong H, Zhou W, Lin W, Tian Q (2016) Picking deep filter responses for fine-grained image recognition. In: Computer vision and pattern recognition, \u20131142","DOI":"10.1109\/CVPR.2016.128"},{"issue":"4","key":"6915_CR57","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1109\/TIP.2016.2531289","volume":"25","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Wei XS, Wu J, Cai J, Lu J, Nguyen VA, Do MN (2016) Weakly supervised fine-grained categorization with part-based image representation. IEEE Trans Image Process 25(4):1713\u20131725","journal-title":"IEEE Trans Image Process"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-6915-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-018-6915-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-6915-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:36:22Z","timestamp":1662510982000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-018-6915-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,28]]},"references-count":57,"journal-issue":{"issue":"21-22","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["6915"],"URL":"https:\/\/doi.org\/10.1007\/s11042-018-6915-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2018,11,28]]},"assertion":[{"value":"30 July 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}