{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T16:44:26Z","timestamp":1748537066710},"reference-count":16,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2018]]},"DOI":"10.1587\/transinf.2017edl8131","type":"journal-article","created":{"date-parts":[[2017,12,31]],"date-time":"2017-12-31T17:29:53Z","timestamp":1514741393000},"page":"273-276","source":"Crossref","is-referenced-by-count":1,"title":["Learning Deep Relationship for Object Detection"],"prefix":"10.1587","volume":"E101.D","author":[{"given":"Nuo","family":"XU","sequence":"first","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlei","family":"HUO","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] M. Kampffmeyer, A.-B. Salberg, and R. Jenssen, \u201cSemantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.680-688, 2016. 10.1109\/cvprw.2016.90","DOI":"10.1109\/CVPRW.2016.90"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] G. Cheng and J. Han, \u201cA survey on object detection in optical remote sensing images,\u201d ISPRS Journal of Photogrammetry and Remote Sensing, vol.117, pp.11-28, 2016. 10.1016\/j.isprsjprs.2016.03.014","DOI":"10.1016\/j.isprsjprs.2016.03.014"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] G. Cheng, P. Zhou, X. Yao, C. Yao, Y. Zhang, and J. Han, \u201cObject detection in VHR optical remote sensing images via learning rota-tion-invariant HOG feature,\u201d IEEE International Workshop on Earth Observation and Remote Sensing Applications, pp.433-436, 2016. 10.1109\/eorsa.2016.7552845","DOI":"10.1109\/EORSA.2016.7552845"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] G. Cheng, P. Zhou, and J. Han, \u201cLearning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images,\u201d IEEE Trans. Geosci. Remote Sens., vol.54, no.12, pp.7405-7415, 2016. 10.1109\/tgrs.2016.2601622","DOI":"10.1109\/TGRS.2016.2601622"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] Y. Cao, X. Niu, and Y. Dou, \u201cRegion-based convolutional neural networks for object detection in very high resolution remote sensing images,\u201d IEEE International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp.548-554, 2016. 10.1109\/fskd.2016.7603232","DOI":"10.1109\/FSKD.2016.7603232"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] W. Diao, X. Sun, X. Zheng, F. Dou, H. Wang, and K. Fu, \u201cEfficient saliency-based object detection in remote sensing images using deep belief networks,\u201d IEEE Geosci. Remote Sens. Lett., vol.13, no.2, pp.137-141, 2016. 10.1109\/lgrs.2015.2498644","DOI":"10.1109\/LGRS.2015.2498644"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J. Bromley, J.W. Bentz, L. Bottou, I. Guyon, Y. Lecun, C. Moore, E. S\u00e4ckinger, and R. Shah, \u201cSignature verification using a\u201c siamese\u201d time delay neural network,\u201d Advances in Neural Information Processing Systems, vol.6, pp.737-744, 1994. 10.1142\/9789812797926_0003","DOI":"10.1142\/9789812797926_0003"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] P. Agrawal, J. Carreira, and J. Malik, \u201cLearning to see by moving,\u201d IEEE International Conference on Computer Vision, pp.37-45, 2015. 10.1109\/iccv.2015.13","DOI":"10.1109\/ICCV.2015.13"},{"key":"9","unstructured":"[9] M.D. Zeiler, \u201cAdadelta: an adaptive learning rate method,\u201d arXiv preprint arXiv:1212.5701, 2012."},{"key":"10","unstructured":"[10] R.E. Fan, P.H. Chen, and C.J. Lin, \u201cWorking set selection using second order information for training support vector machines,\u201d Journal of Machine Learning Research, vol.6, no.Dec, pp.1889-1918, 2005."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] F. Wang, W. Zuo, L. Zhang, D. Meng, and D. Zhang, \u201cA kernel classification framework for metric learning,\u201d IEEE Trans. Neural Netw. Learning Syst. vol.26, no.9, pp.1950-1962, 2015. 10.1109\/tnnls.2014.2361142","DOI":"10.1109\/TNNLS.2014.2361142"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] N. Segata and E. Blanzieri, \u201cFast local support vector machines for large datasets,\u201d International Workshop on Machine Learning and Data Mining in Pattern Recognition, vol.5632, pp.295-310, 2009. 10.1007\/978-3-642-03070-3_22","DOI":"10.1007\/978-3-642-03070-3_22"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] E. Tola, V. Lepetit, and P. Fua, \u201cDaisy: An efficient dense descriptor applied to wide-baseline stereo,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.32, no.5, pp.815-830, 2010. 10.1109\/tpami.2009.77","DOI":"10.1109\/TPAMI.2009.77"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer, \u201cDiscriminative learning of deep convolutional feature point descriptors,\u201d IEEE International Conference on Computer Vision, pp.118-126, 2015. 10.1109\/iccv.2015.22","DOI":"10.1109\/ICCV.2015.22"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] R. Girshick, J. Donahue, T. Darrell, and J. Malik, \u201cRich feature hierarchies for accurate object detection and semantic segmentation,\u201d IEEE conference on computer vision and pattern recognition, pp.580-587, 2014. 10.1109\/cvpr.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] P.F. Felzenszwalb and D.P. Huttenlocher, \u201cEfficient graph-based image segmentation,\u201d Int. J. Comput. Vision., vol.59, no.2, pp.167-181, 2004. 10.1023\/b:visi.0000022288.19776.77","DOI":"10.1023\/B:VISI.0000022288.19776.77"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/1\/E101.D_2017EDL8131\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,8]],"date-time":"2019-10-08T18:23:51Z","timestamp":1570559031000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/1\/E101.D_2017EDL8131\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":16,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2017edl8131","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}