{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:25:55Z","timestamp":1775579155768,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T00:00:00Z","timestamp":1517875200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key R&D Program of China","award":["No.2017YFB1002201"],"award-info":[{"award-number":["No.2017YFB1002201"]}]},{"name":"National Natural Science Fund for Distinguished Young Scholar","award":["Grant No. 61625204"],"award-info":[{"award-number":["Grant No. 61625204"]}]},{"name":"The State Key Program of National Science Foundation of China","award":["Grant No.61432012"],"award-info":[{"award-number":["Grant No.61432012"]}]},{"name":"The State Key Program of National Science Foundation of China","award":["61432014"],"award-info":[{"award-number":["61432014"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s00500-018-3051-y","type":"journal-article","created":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T03:23:04Z","timestamp":1517887384000},"page":"4041-4050","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An angle-based method for measuring the semantic similarity between visual and textual features"],"prefix":"10.1007","volume":"23","author":[{"given":"Chenwei","family":"Tang","sequence":"first","affiliation":[]},{"given":"Jiancheng","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Yao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jixiang","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,2,6]]},"reference":[{"issue":"7","key":"3051_CR1","first-page":"1425","volume":"38","author":"Z Akata","year":"2015","unstructured":"Akata Z, Perronnin F, Harchaoui Z, Schmid C (2015a) Label-embedding for image classification. IEEE Trans Softw Eng 38(7):1425\u20131438","journal-title":"IEEE Trans Softw Eng"},{"key":"3051_CR2","doi-asserted-by":"crossref","unstructured":"Akata Z, Reed S, Walter D, Lee H (2015b) Evaluation of output embeddings for fine-grained image classification. In: IEEE Computer Vision and Pattern Recognition, pp 2927\u20132936","DOI":"10.1109\/CVPR.2015.7298911"},{"key":"3051_CR3","doi-asserted-by":"crossref","unstructured":"Antol S, Agrawal A, Lu J, Mitchell M, Batra D, Zitnick CL, Parikh D (2015) VQA: visual question answering. In: IEEE International Conference on Computer Vision, pp 2425\u20132433","DOI":"10.1109\/ICCV.2015.279"},{"key":"3051_CR4","first-page":"211","volume-title":"Weighted attribute combinations based similarity measures","author":"M Baioletti","year":"2012","unstructured":"Baioletti M, Coletti G, Petturiti D (2012) Weighted attribute combinations based similarity measures. Springer, Berlin, pp 211\u2013220"},{"issue":"6","key":"3051_CR5","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s00500-007-0229-0","volume":"12","author":"CH Chen","year":"2008","unstructured":"Chen CH, Lin CJ, Lin CT (2008) An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation. Soft Comput 12(6):567\u2013583","journal-title":"Soft Comput"},{"key":"3051_CR6","doi-asserted-by":"crossref","unstructured":"Chen D, Lv JC, Yi Z (2014) A local non-negative pursuit method for intrinsic manifold structure preservation. In: The 28th AAAI Conference on Artificial Intelligence (AAAI), vol 3, pp 1745\u20131751","DOI":"10.1609\/aaai.v28i1.8966"},{"key":"3051_CR7","unstructured":"Dehak N, Dehak R, Glass J, Reynolds D, Kenny P (2010) Cosine similarity scoring without score normalization techniques. In: Proceedings of Odyssey 2010\u2014The Speaker and Language Recognition Workshop"},{"key":"3051_CR8","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Li FF (2009) Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp 248\u2013255"},{"issue":"1","key":"3051_CR9","first-page":"815","volume":"50","author":"J Donahue","year":"2013","unstructured":"Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2013) Decaf: a deep convolutional activation feature for generic visual recognition. Comput Sci 50(1):815\u2013830","journal-title":"Comput Sci"},{"key":"3051_CR10","doi-asserted-by":"crossref","unstructured":"Fang H, Gupta S, Iandola F, Srivastava RK (2015) From captions to visual concepts and back. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1473\u20131482","DOI":"10.1109\/CVPR.2015.7298754"},{"key":"3051_CR11","unstructured":"Gao H, Mao J, Zhou J, Huang Z, Wang L, Xu W (2015) Are you talking to a machine? Dataset and methods for multilingual image question answering. Computer science, pp 2296\u20132304"},{"issue":"2","key":"3051_CR12","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/s11263-013-0658-4","volume":"106","author":"Y Gong","year":"2014","unstructured":"Gong Y, Ke Q, Isard M, Lazebnik S (2014a) A multi-view embedding space for modeling internet images, tags, and their semantics. Int J Comput Vis 106(2):210\u2013233","journal-title":"Int J Comput Vis"},{"key":"3051_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10593-2_35","volume-title":"Improving image-sentence embeddings using large weakly annotated photo collections","author":"Y Gong","year":"2014","unstructured":"Gong Y, Wang L, Hodosh M, Hockenmaier J, Lazebnik S (2014b) Improving image-sentence embeddings using large weakly annotated photo collections. Springer, Berlin"},{"key":"3051_CR14","unstructured":"Goyal MM, Agrawal N, Sarma MK, Kalita NJ (2015) Comparison clustering using cosine and fuzzy set based similarity measures of text documents. Computer science"},{"issue":"8","key":"3051_CR15","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"A Graves","year":"1997","unstructured":"Graves A (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"3051_CR16","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. International Conference on International Conference on Machine Learning, pp 448\u2013456"},{"key":"3051_CR17","doi-asserted-by":"crossref","unstructured":"Karpathy A, Li FF (2015) Deep visual-semantic alignments for generating image descriptions. Eprint Arxiv, pp 3128\u20133137","DOI":"10.1109\/CVPR.2015.7298932"},{"issue":"2","key":"3051_CR18","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1103\/PhysRevD.52.1108","volume":"52","author":"A Kempf","year":"1994","unstructured":"Kempf A (1994) Hilbert space representation of the minimal length uncertainty relation. Phys Rev D Part Fields 52(2):1108\u20131118","journal-title":"Phys Rev D Part Fields"},{"key":"3051_CR19","doi-asserted-by":"crossref","unstructured":"Kulis B, Saenko K, Darrell T (2011) What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: Computer Vision and Pattern Recognition, pp 1785\u20131792","DOI":"10.1109\/CVPR.2011.5995702"},{"issue":"3","key":"3051_CR20","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/TPAMI.2013.140","volume":"36","author":"CH Lampert","year":"2014","unstructured":"Lampert CH, Nickisch H, Harmeling S (2014) Attribute-based classification for zero-shot visual object categorization. IEEE Trans Pattern Anal Mach Intell 36(3):453\u2013465","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3051_CR21","unstructured":"Larochelle H, Erhan D, Bengio Y (2008) Zero-data learning of new tasks. In: Proceedings of the National Conference on Artificial Intelligence. vol 2, pp 46\u2013651"},{"issue":"3","key":"3051_CR22","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1007\/s00500-014-1292-y","volume":"19","author":"SH Liao","year":"2015","unstructured":"Liao SH, Hsieh JG, Chang JY, Lin CT (2015) Training neural networks via simplified hybrid algorithm mixing Nelder\u2013Mead and particle swarm optimization methods. Soft Comput 19(3):679\u2013689","journal-title":"Soft Comput"},{"issue":"6","key":"3051_CR23","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1109\/TNN.2007.895824","volume":"18","author":"JC Lv","year":"2007","unstructured":"Lv JC, Yi Z, Tan KK (2007) Global convergence of GHA learning algorithm with nonzero-approaching learning rates. IEEE Trans Neural Netw TNN 18(6):1557\u20131571","journal-title":"IEEE Trans Neural Netw TNN"},{"key":"3051_CR24","volume-title":"Subspace learning of neural networks","author":"JC Lv","year":"2010","unstructured":"Lv JC, Yi Z, Zhou J (2010) Subspace learning of neural networks, vol 42. CRC Press, Boca Raton"},{"key":"3051_CR25","unstructured":"Mao J, Xu W, Yang Y, Wang J, Huang Z, Yuille A (2015) Deep captioning with multimodal recurrent neural networks (m-rnn). Eprint Arxiv"},{"key":"3051_CR26","unstructured":"Nair V, Hinton GE (2015) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the ICML, pp 807\u2013814"},{"key":"3051_CR27","first-page":"709","volume-title":"Cosine similarity metric learning for face verification","author":"HV Nguyen","year":"2010","unstructured":"Nguyen HV, Bai L (2010) Cosine similarity metric learning for face verification. Springer, Berlin, pp 709\u2013720"},{"key":"3051_CR28","unstructured":"Nilsback ME, Zisserman A (2008) Automated flower classification over a large number of classes. Computer Vision, Graphics & Image Processing, 2008. ICVGIP \u201908. Sixth Indian Conference on, pp 722\u2013729"},{"key":"3051_CR29","unstructured":"Palatucci M, Pomerleau D, Hinton GE, Mitchell TM (2009) Zero-shot learning with semantic output codes. In: Advances in neural information processing systems. International Conference on Neural Information Processing Systems, pp 1410\u20131418"},{"key":"3051_CR30","doi-asserted-by":"crossref","unstructured":"Reed S, Akata Z, Lee H, Schiele B (2016) Learning deep representations of fine-grained visual descriptions. Computer Vision and Pattern Recognition, pp 49\u201358","DOI":"10.1109\/CVPR.2016.13"},{"key":"3051_CR31","unstructured":"Romera-Paredes B, Torr PHS (2015) An embarrassingly simple approach to zero-shot learning. In: International Conference on Machine Learning, pp 2152\u20132161"},{"key":"3051_CR32","unstructured":"Shum S, Dehak N, Dehak R, Glass JR (2010) Unsupervised speaker adaptation based on the cosine similarity for textindependent speaker verification. In: Proceedings of Odyssey"},{"key":"3051_CR33","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going deeper with convolutions. In: Computer vision and pattern recognition, pp 1\u20139"},{"issue":"2","key":"3051_CR34","first-page":"1453","volume":"6","author":"I Tsochantaridis","year":"2005","unstructured":"Tsochantaridis I, Joachims T, Hofmann T, Altun Y (2005) Large margin methods for structured and interdependent output variables. J Mach Learn Res 6(2):1453\u20131484","journal-title":"J Mach Learn Res"},{"key":"3051_CR35","unstructured":"Visa S, Ramsay B, Ralescu AL, Knaap EVD (2011) Confusion matrix-based feature selection. In: Midwest Artificial Intelligence and Cognitive Science Conference 2011, Cincinnati, Ohio, USA, April, pp 120\u2013127"},{"key":"3051_CR36","doi-asserted-by":"crossref","unstructured":"Wang L, Li Y, Lazebnik S (2015) Learning deep structure-preserving image\u2013text embeddings. Computer Science","DOI":"10.1109\/CVPR.2016.541"},{"issue":"2","key":"3051_CR37","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s00500-014-1272-2","volume":"19","author":"J Wei","year":"2015","unstructured":"Wei J, Lv JC, Yi Z (2015) Robust classifier using distance-based representation with square weights. Soft Comput 19(2):507\u2013515","journal-title":"Soft Comput"},{"key":"3051_CR38","unstructured":"Welinder P, Branson S, Mita T, Wah C, Schroff F, Belongie S, Perona P (2010) Caltech-UCSD birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology"},{"key":"3051_CR39","unstructured":"Xie C, Lv J, Li X (2016) Finding a good initial configuration of parameters for restricted Boltzmann machine pre-training. Soft Computing, pp 1\u20139"},{"issue":"1","key":"3051_CR40","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.mcm.2010.07.022","volume":"53","author":"J Ye","year":"2011","unstructured":"Ye J (2011) Cosine similarity measures for intuitionistic fuzzy sets and their applications. Math Comput Model 53(1):91\u201397","journal-title":"Math Comput Model"},{"key":"3051_CR41","unstructured":"Zhang X, Zhao J, Lecun Y (2015) Character-level convolutional networks for text classification. In: NIPS\u201915 Proceedings of the 28th International Conference on Neural Information Processing Systems. vol 1, pp 649\u2013657"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-018-3051-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-018-3051-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-018-3051-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T23:18:16Z","timestamp":1660432696000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-018-3051-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,6]]},"references-count":41,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["3051"],"URL":"https:\/\/doi.org\/10.1007\/s00500-018-3051-y","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,6]]},"assertion":[{"value":"6 February 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare that we have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}