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Zhang, and H.-Y. Yang, \u201cContent-based image retrieval by integrating color and texture features,\u201d Multimedia tools and applications, vol.68, no.3, pp.545-569, 2014. 10.1007\/s11042-012-1055-7","DOI":"10.1007\/s11042-012-1055-7"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] J.-M. Guo, H. Prasetyo, and J.-H. Chen, \u201cContent-based image retrieval using error diffusion block truncation coding features,\u201d IEEE Transactions on Circuits and Systems for Video Technology, vol.25, no.3, pp.466-481, 2014. 10.1109\/tcsvt.2014.2358011","DOI":"10.1109\/TCSVT.2014.2358011"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] X. Duanmu, \u201cImage retrieval using color moment invariant,\u201d 2010 Seventh International Conference on Information Technology: New Generations, pp.200-203, IEEE, 2010. 10.1109\/itng.2010.231","DOI":"10.1109\/ITNG.2010.231"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] H. Shao, Y. Wu, W. Cui, and J. Zhang, \u201cImage retrieval based on mpeg-7 dominant color descriptor,\u201d 2008 The 9th International Conference for Young Computer Scientists, pp.753-757, IEEE, 2008. 10.1109\/icycs.2008.89","DOI":"10.1109\/ICYCS.2008.89"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] M.M. Islam, D. Zhang, and G. Lu, \u201cAutomatic categorization of image regions using dominant color based vector quantization,\u201d 2008 Digital Image Computing: Techniques and Applications, pp.191-198, IEEE, 2008. 10.1109\/dicta.2008.17","DOI":"10.1109\/DICTA.2008.17"},{"key":"6","unstructured":"[6] D. ping Tian et al., \u201cA review on image feature extraction and representation techniques,\u201d International Journal of Multimedia and Ubiquitous Engineering, vol.8, no.4, pp.385-396, 2013."},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] G.A. Papakostas, D.E. Koulouriotis, and V.D. Tourassis, \u201cFeature extraction based on wavelet moments and moment invariants in machine vision systems,\u201d Human-Centric Machine Vision, p.31, 2012. 10.5772\/33141","DOI":"10.5772\/33141"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Z. Mehmood, S.M. Anwar, N. Ali, H.A. Habib, and M. Rashid, \u201cA novel image retrieval based on a combination of local and global histograms of visual words,\u201d Mathematical Problems in Engineering, vol.2016, 2016. 10.1155\/2016\/8217250","DOI":"10.1155\/2016\/8217250"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] B. Zafar, R. Ashraf, N. Ali, M. Ahmed, S. Jabbar, and S.A. Chatzichristofis, \u201cImage classification by addition of spatial information based on histograms of orthogonal vectors,\u201d PloS one, vol.13, no.6, 2018. 10.1371\/journal.pone.0198175","DOI":"10.1371\/journal.pone.0198175"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] N. Ali, K.B. Bajwa, R. Sablatnig, and Z. Mehmood, \u201cImage retrieval by addition of spatial information based on histograms of triangular regions,\u201d Computers & Electrical Engineering, vol.54, pp.539-550, 2016. 10.1016\/j.compeleceng.2016.04.002","DOI":"10.1016\/j.compeleceng.2016.04.002"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] A. Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImageNet classification with deep convolutional neural networks,\u201d Communications of the ACM, ACM New York, NY, USA, vol.60, no.6, pp.84-90, 2017. 10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"12","unstructured":"[12] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d arXiv preprint arXiv:1409.1556, 2014."},{"key":"13","unstructured":"[13] A.B. Babenko and V. Lempitsky, \u201cAggregating local deep features for image retrieval,\u201d The IEEE International Conference on Computer Vision (ICCV), Dec. 2015. 10.1109\/iccv.2015.150"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] H. Azizpour, A.S. Razavian, J. Sullivan, A. Maki, and S. Carlsson, \u201cFrom generic to specific deep representations for visual recognition,\u201d Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp.36-45, 2015. 10.1109\/cvprw.2015.7301270","DOI":"10.1109\/CVPRW.2015.7301270"},{"key":"15","unstructured":"[15] G. Tolias, R. Sicre, and H. J\u00e9gou, \u201cParticular object retrieval with integral max-pooling of cnn activations,\u201d arXiv preprint arXiv:1511.05879, 2015."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] D.G. Lowe, \u201cObject recognition from local scale-invariant features,\u201d The Proceedings of the Seventh IEEE International Conference on Computer vision, pp.1150-1157, IEEE, 1999. 10.1109\/iccv.1999.790410","DOI":"10.1109\/ICCV.1999.790410"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] N. Dalal and B. Triggs, \u201cHistograms of oriented gradients for human detection,\u201d IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp.886-893, IEEE, 2005. 10.1109\/cvpr.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] J. Sivic and A. Zisserman, \u201cVideo google: A text retrieval approach to object matching in videos,\u201d Proceedings Ninth IEEE International Conference on Computer Vision, 2003. 10.1109\/iccv.2003.1238663","DOI":"10.1109\/ICCV.2003.1238663"},{"key":"19","unstructured":"[19] J. Yang, K. Yu, Y. Gong, and T. Huang, \u201cLinear spatial pyramid matching using sparse coding for image classification,\u201d IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp.1794-1801, IEEE, 2009. 10.1109\/cvprw.2009.5206757"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] F. Perronnin, J. S\u00e1nchez, and T. Mensink, \u201cImproving the fisher kernel for large-scale image classification,\u201d Computer Vision-ECCV 2010, vol.6314, pp.143-156, 2010. 10.1007\/978-3-642-15561-1_11","DOI":"10.1007\/978-3-642-15561-1_11"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] H. Jegou, F. Perronnin, M. Douze, J. S\u00e1nchez, P. Perez, and C. Schmid, \u201cAggregating local image descriptors into compact codes,\u201d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, no.9, pp.1704-1716, 2012. 10.1109\/tpami.2011.235","DOI":"10.1109\/TPAMI.2011.235"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] Z. Cai, W. Gao, Z. Yu, J. Huang, and Z. Cai, \u201cFeature extraction with triplet convolutional neural network for content-based image retrieval,\u201d 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp.337-342, IEEE, 2017. 10.1109\/iciea.2017.8282867","DOI":"10.1109\/ICIEA.2017.8282867"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] F. Yan, K. Mikolajczyk, and J. Kittler, \u201cMultiple kernel learning via distance metric learning for interactive image retrieval,\u201d International Workshop on Multiple Classifier Systems, vol.6713, pp.147-156, Springer, 2011. 10.1007\/978-3-642-21557-5_17","DOI":"10.1007\/978-3-642-21557-5_17"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] J. Zhou, X. Liu, W. Liu, and J. Gan, \u201cImage retrieval based on effective feature extraction and diffusion process,\u201d Multimedia Tools and Applications, vol.78, no.5, pp.6163-6190, 2019. 10.1007\/s11042-018-6192-1","DOI":"10.1007\/s11042-018-6192-1"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] Y. Gong, L. Wang, R. Guo, and S. Lazebnik, \u201cMulti-scale orderless pooling of deep convolutional activation features,\u201d European conference on computer vision, vol.8695, pp.392-407, Springer, 2014. 10.1007\/978-3-319-10584-0_26","DOI":"10.1007\/978-3-319-10584-0_26"},{"key":"26","unstructured":"[26] A. Sharif Razavian, J. Sullivan, A. Maki, and S. Carlsson, \u201cA baseline for visual instance retrieval with deep convolutional networks,\u201d International Conference on Learning Representations, May 7-9, 2015, San Diego, CA, ICLR, 2015."},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] V. Chandrasekhar, J. Lin, O. Mor\u00e8re, H. Goh, and A. Veillard, \u201cA practical guide to cnns and fisher vectors for image instance retrieval,\u201d Signal Processing, vol.128, pp.426-439, 2016. 10.1016\/j.sigpro.2016.05.021","DOI":"10.1016\/j.sigpro.2016.05.021"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] D. Marcos, M. Volpi, N. Komodakis, and D. Tuia, \u201cRotation equivariant vector field networks,\u201d Proceedings of the IEEE International Conference on Computer Vision, pp.5048-5057, 2017. 10.1109\/iccv.2017.540","DOI":"10.1109\/ICCV.2017.540"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] L. Sifre and S. Mallat, \u201cRotation, scaling and deformation invariant scattering for texture discrimination,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1233-1240, 2013. 10.1109\/cvpr.2013.163","DOI":"10.1109\/CVPR.2013.163"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] G. Cheng, P. Zhou, and J. Han, \u201cRifd-cnn: Rotation-invariant and fisher discriminative convolutional neural networks for object detection,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2884-2893, 2016. 10.1109\/cvpr.2016.315","DOI":"10.1109\/CVPR.2016.315"},{"key":"31","unstructured":"[31] M. Jaderberg, K. Simonyan, A. Zisserman, et al., \u201cSpatial transformer networks,\u201d Advances in neural information processing systems, pp.2017-2025, 2015."},{"key":"32","unstructured":"[32] P.Y. Simard, D. Steinkraus, and J.C. Platt, \u201cBest practices for convolutional neural networks applied to visual document analysis,\u201d ICDAR, 2003. 10.1109\/icdar.2003.1227801"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, \u201cObject retrieval with large vocabularies and fast spatial matching,\u201d IEEE Conference on Computer Vision and Pattern Recognition, CVPR'07, pp.1-8, IEEE, 2007. 10.1109\/cvpr.2007.383172","DOI":"10.1109\/CVPR.2007.383172"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, \u201cLost in quantization: Improving particular object retrieval in large scale image databases,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008. 10.1109\/cvpr.2008.4587635","DOI":"10.1109\/CVPR.2008.4587635"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] F. Radenovi\u0107, G. Tolias, and O. Chum, \u201cCnn image retrieval learns from bow: Unsupervised fine-tuning with hard examples,\u201d European Conference on Computer Vision, vol.9905, pp.3-20, Springer, 2016. 10.1007\/978-3-319-46448-0_1","DOI":"10.1007\/978-3-319-46448-0_1"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] G. Huang, Z. Liu, L. Van Der Maaten, and K.Q. Weinberger, \u201cDensely connected convolutional networks,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4700-4708, 2017. 10.1109\/cvpr.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] G. Takacs, V. Chandrasekhar, S. Tsai, D. Chen, R. Grzeszczuk, and B. Girod, \u201cUnified real-time tracking and recognition with rotation-invariant fast features,\u201d 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.934-941, IEEE, 2010. 10.1109\/cvpr.2010.5540116","DOI":"10.1109\/CVPR.2010.5540116"},{"key":"39","unstructured":"[39] K.Q. Weinberger, J. Blitzer, and L.K. Saul, \u201cDistance metric learning for large margin nearest neighbor classification,\u201d Advances in neural information processing systems, pp.1473-1480, 2006."},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] J. Wang, Y. Song, T. Leung, C. Rosenberg, J. Wang, J. Philbin, B. Chen, and Y. Wu, \u201cLearning fine-grained image similarity with deep ranking,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1386-1393, 2014. 10.1109\/cvpr.2014.180","DOI":"10.1109\/CVPR.2014.180"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] E. Hoffer and N. Ailon, \u201cDeep metric learning using triplet network,\u201d International Workshop on Similarity-Based Pattern Recognition, pp.84-92, Springer, 2015. 10.1007\/978-3-319-24261-3_7","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] F. Schroff, D. Kalenichenko, and J. Philbin, \u201cFacenet: A unified embedding for face recognition and clustering,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.815-823, 2015. 10.1109\/cvpr.2015.7298682","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] M.D. Zeiler and R. Fergus, \u201cVisualizing and understanding convolutional networks,\u201d European conference on computer vision, vol.8689, pp.818-833, Springer, 2014. 10.1007\/978-3-319-10590-1_53","DOI":"10.1007\/978-3-319-10590-1_53"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/1\/E104.D_2020EDP7017\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T03:26:20Z","timestamp":1609557980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/1\/E104.D_2020EDP7017\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,1]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2020edp7017","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,1]]}}}