{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T01:55:03Z","timestamp":1780365303818,"version":"3.54.1"},"reference-count":23,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2016]]},"DOI":"10.1587\/transinf.2015edl8212","type":"journal-article","created":{"date-parts":[[2016,5,31]],"date-time":"2016-05-31T22:12:35Z","timestamp":1464732755000},"page":"1711-1715","source":"Crossref","is-referenced-by-count":23,"title":["Food Image Recognition Using Covariance of Convolutional Layer Feature Maps"],"prefix":"10.1587","volume":"E99.D","author":[{"given":"Atsushi","family":"TATSUMA","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Toyohashi University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaki","family":"AONO","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Toyohashi University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] K. Aizawa, \u201cMultimedia FoodLog: Diverse applications from self-monitoring to social contributions,\u201d ITE Trans. Media Technology and Applications, vol.1, no.3, pp.214-219, 2013.","DOI":"10.3169\/mta.1.214"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] Y. Kawano and K. Yanai, \u201cFoodCam: A real-time food recognition system on a smartphone,\u201d Multimedia Tools and Applications, vol.74, no.14, pp.5263-5287, 2015.","DOI":"10.1007\/s11042-014-2000-8"},{"key":"3","unstructured":"[3] A. Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImageNet classification with deep convolutional neural networks,\u201d Advances in Neural Information Processing Systems, NIPS'12, vol.25, pp.1097-1105, 2012."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] L. Bossard, M. Guillaumin, and L. Van Gool, \u201cFood-101-Mining discriminative components with random forests,\u201d Proc. 13th European Conference on Computer Vision, ECCV'14, pp.446-461, 2014.","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] S. Lazebnik, C. Schmid, and J. Ponce, \u201cBeyond bags of features: Spatial pyramid matching for recognizing natural scene categories,\u201d Proc. 2006 IEEE Conference on Computer Vision and Pattern Recognition, CVPR'06, vol.2, pp.2169-2178, 2006.","DOI":"10.1109\/CVPR.2006.68"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. S\u00e1nchez, F. Perronnin, T. Mensink, and J. Verbeek, \u201cImage classification with the fisher vector: Theory and practice,\u201d International Journal of Computer Vision, vol.105, no.3, pp.222-245, 2013.","DOI":"10.1007\/s11263-013-0636-x"},{"key":"7","unstructured":"[7] J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, \u201cDeCAF: A deep convolutional activation feature for generic visual recognition,\u201d Proc. 31st International Conference on Machine Learning, ICML'14, pp.647-655, 2014."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] A.S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, \u201cCNN features off-the-shelf: An astounding baseline for recognition,\u201d Proc. 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW '14, pp.512-519, 2014.","DOI":"10.1109\/CVPRW.2014.131"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] M.D. Zeiler and R. Fergus, \u201cVisualizing and understanding convolutional networks,\u201d Proc. 13th European Conference on Computer Vision, ECCV'14, pp.818-833, 2014.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] Y. Kawano and K. Yanai, \u201cFood image recognition with deep convolutional features,\u201d Proc. 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp'14, pp.589-593, 2014.","DOI":"10.1145\/2638728.2641339"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] P.-H. Gosselin, N. Murray, H. J\u00e9gou, and F. Perronnin, \u201cRevisiting the fisher vector for fine-grained classification,\u201d Pattern Recognition Letters, vol.49, pp.92-98, Nov. 2014.","DOI":"10.1016\/j.patrec.2014.06.011"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] O. Tuzel, F. Porikli, and P. Meer, \u201cRegion covariance: A fast descriptor for detection and classification,\u201d Proc. 9th European Conference on Computer Vision, ECCV'06, vol.3952, pp.589-600, 2006.","DOI":"10.1007\/11744047_45"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] D. Tosato, M. Spera, M. Cristani, and V. Murino, \u201cCharacterizing humans on riemannian manifolds,\u201d IEEE Trans. Pattern Analysis and Machine Intelligence, vol.35, no.8, pp.1972-1984, Aug. 2013.","DOI":"10.1109\/TPAMI.2012.263"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] G. Serra, C. Grana, M. Manfredi, and R. Cucchiara, \u201cCovariance of covariance features for image classification,\u201d Proc. International Conference on Multimedia Retrieval, ICMR '14, pp.411-414, 2014.","DOI":"10.1145\/2578726.2578781"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] X. Pennec, P. Fillard, and N. Ayache, \u201cA riemannian framework for tensor computing,\u201d International Journal of Computer Vision, vol.66, no.1, pp.41-66, 2006.","DOI":"10.1007\/s11263-005-3222-z"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] H. J\u00e9gou and O. Chum, \u201cNegative evidences and co-occurences in image retrieval: The benefit of PCA and whitening,\u201d Proc. 12th European Conference on Computer Vision, ECCV'12, vol.7573, pp.774-787, 2012.","DOI":"10.1007\/978-3-642-33709-3_55"},{"key":"17","unstructured":"[17] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, \u201cOverFeat: Integrated recognition, localization and detection using convolutional networks,\u201d CoRR, vol.abs\/1312.6229, 2013."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] J. Deng, W. Dong, R. Socher, L.J. Li, K. Li, and L. Fei-Fei, \u201cImageNet: A large-scale hierarchical image database,\u201d Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR'09, pp.248-255, 2009.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"19","unstructured":"[19] R.E. Fan, K.W. Chang, C.J. Hsieh, X.R. Wang, and C.J. Lin, \u201cLIBLINEAR: A library for large linear classification,\u201d J. Machine Learning Research, vol.9, pp.1871-1874, June 2008."},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] S. Singh, A. Gupta, and A.A. Efros, \u201cUnsupervised discovery of mid-level discriminative patches,\u201d Proc. 12th European Conference on Computer Vision, ECCV'12, vol.7573, pp.73-86, 2012.","DOI":"10.1007\/978-3-642-33709-3_6"},{"key":"21","unstructured":"[21] X. Wang, D. Kumar, N. Thome, M. Cord, and F. Precioso, \u201cRecipe recognition with large multimodal food dataset,\u201d Proc. 2015 IEEE Conference on Multimedia Expo Workshops, ICMEW'15, pp.1-6, June 2015."},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] W. Zou, X. Wang, M. Sun, and Y. Lin, \u201cGeneric object detection with dense neural patterns and regionlets,\u201d Proc. 25th British Machine Vision Conference, BMVC'14, pp.1-11, 2014.","DOI":"10.5244\/C.28.72"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] L. Liu, C. Shen, and A. van den Hengel, \u201cThe treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification,\u201d Proc. 2015 IEEE Conference on Computer Vision and Pattern Recognition, CVPR'15, June 2015.","DOI":"10.1109\/CVPR.2015.7299107"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E99.D\/6\/E99.D_2015EDL8212\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,24]],"date-time":"2017-06-24T15:26:47Z","timestamp":1498318007000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E99.D\/6\/E99.D_2015EDL8212\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":23,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2016]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2015edl8212","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]}}}