{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:19:52Z","timestamp":1775261992076,"version":"3.50.1"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030033378","type":"print"},{"value":"9783030033385","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-03338-5_27","type":"book-chapter","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T23:57:42Z","timestamp":1541116662000},"page":"316-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature Aggregation Tree: Capture Temporal Motion Information for Action Recognition in Videos"],"prefix":"10.1007","author":[{"given":"Bing","family":"Zhu","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,3]]},"reference":[{"issue":"2\u20133","key":"27_CR1","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11263-005-1838-7","volume":"64","author":"I Laptev","year":"2005","unstructured":"Laptev, I., Lindeberg, T.: On space-time interest points. Int. J. Comput. Vis. 64(2\u20133), 107\u2013123 (2005)","journal-title":"Int. J. Comput. Vis."},{"issue":"1","key":"27_CR2","first-page":"20","volume":"22","author":"L Wang","year":"2016","unstructured":"Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. ACM Trans. Inf. Syst. 22(1), 20\u201336 (2016)","journal-title":"ACM Trans. Inf. Syst."},{"key":"27_CR3","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: International Conference on Neural Information Processing Systems, pp. 568\u2013576 (2014)"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks, pp. 4489\u20134497 (2014)","DOI":"10.1109\/ICCV.2015.510"},{"issue":"1","key":"27_CR5","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221\u2013231 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"99","key":"27_CR6","first-page":"1","volume":"PP","author":"G Varol","year":"2016","unstructured":"Varol, G., Laptev, I., Schmid, C.: Long-term temporal convolutions for action recognition. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"27_CR7","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1109\/TII.2011.2172452","volume":"8","author":"X Cao","year":"2012","unstructured":"Cao, X., Ning, B., Yan, P., Li, X.: Selecting key poses on manifold for pairwise action recognition. IEEE Trans. Ind. Inform. 8(1), 168\u2013177 (2012)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"6","key":"27_CR8","doi-asserted-by":"publisher","first-page":"1860","DOI":"10.1109\/TSMCB.2012.2231959","volume":"43","author":"L Liu","year":"2013","unstructured":"Liu, L., Shao, L., Zhen, X., Li, X.: Learning discriminative key poses for action recognition. IEEE Trans. Cybern. 43(6), 1860\u20131870 (2013)","journal-title":"IEEE Trans. Cybern."},{"issue":"3","key":"27_CR9","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1109\/TPAMI.2011.147","volume":"34","author":"Z Jiang","year":"2012","unstructured":"Jiang, Z., Lin, Z., Davis, L.S.: Recognizing human actions by learning and matching shape-motion prototype trees. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 533\u2013547 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Jegou, H., Douze, M., Schmid, C., Perez, P.: Aggregating local descriptors into a compact image representation, pp. 3304\u20133311 (2010)","DOI":"10.1109\/CVPR.2010.5540039"},{"key":"27_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-642-15561-1_11","volume-title":"Computer Vision \u2013 ECCV 2010","author":"F Perronnin","year":"2010","unstructured":"Perronnin, F., S\u00e1nchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143\u2013156. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15561-1_11"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Sydorov, V., Sakurada, M., Lampert, C.H.: Deep fisher kernels - end to end learning of the fisher kernel GMM parameters, pp. 1402\u20131409 (2014)","DOI":"10.1109\/CVPR.2014.182"},{"issue":"C","key":"27_CR13","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.cviu.2016.03.013","volume":"150","author":"X Peng","year":"2016","unstructured":"Peng, X., Wang, L., Wang, X., Qiao, Y.: Bag of visual words and fusion methods for action recognition: comprehensive study and good practice. Comput. Vis. Image Underst. 150(C), 109\u2013125 (2016)","journal-title":"Comput. Vis. Image Underst."},{"key":"27_CR14","unstructured":"Li, F.F., Perona, P.: A Bayesian hierarchical model for learning natural scene categories, pp. 524\u2013531 (2005)"},{"issue":"3","key":"27_CR15","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s11263-015-0846-5","volume":"119","author":"H Wang","year":"2016","unstructured":"Wang, H., Dan, O., Verbeek, J., Schmid, C.: A robust and efficient video representation for action recognition. Int. J. Comput. Vis. 119(3), 219\u2013238 (2016)","journal-title":"Int. J. Comput. Vis."},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors, pp. 4305\u20134314 (2015)","DOI":"10.1109\/CVPR.2015.7299059"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Kar, A., Rai, N., Sikka, K., Sharma, G.: AdaScan: adaptive scan pooling in deep convolutional neural networks for human action recognition in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3376\u20133385 (2017)","DOI":"10.1109\/CVPR.2017.604"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Girdhar, R., Ramanan, D., Gupta, A., Sivic, J., Russell, B.: ActionVLAD: learning spatio-temporal aggregation for action classification, pp. 3165\u20133174 (2017)","DOI":"10.1109\/CVPR.2017.337"},{"key":"27_CR19","unstructured":"Sharma, S., Kiros, R., Salakhutdinov, R.: Action recognition using visual attention. arXiv preprint arXiv:1511.04119 (2015)"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Ng, Y.H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: deep networks for video classification, vol. 16, no. 4, pp. 4694\u20134702 (2015)","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description, pp. 677\u2013691 (2015)","DOI":"10.1109\/TPAMI.2016.2599174"},{"issue":"2","key":"27_CR22","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.cviu.2006.08.002","volume":"104","author":"TB Moeslund","year":"2006","unstructured":"Moeslund, T.B., Hilton, A., Kr\u00fcger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2), 90\u2013126 (2006)","journal-title":"Comput. Vis. Image Underst."},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Han, J., Kamber, M.: Data Mining: Concepts and Techniques, Data Mining Concepts Models Methods & Algorithms, 2nd edn, vol. 5, no. 4, pp. 1\u201318 (2011)","DOI":"10.1002\/9781118029145.ch1"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition, pp. 357\u2013360 (2007)","DOI":"10.1145\/1291233.1291311"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Kl\u00e4ser, A., Marszalek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: British Machine Vision Conference 2008, Leeds, September 2008","DOI":"10.5244\/C.22.99"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Wang, H., Schmid, C.: Action recognition with improved trajectories. In: IEEE International Conference on Computer Vision, pp. 3551\u20133558 (2014)","DOI":"10.1109\/ICCV.2013.441"},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies, pp. 1\u20138 (2008)","DOI":"10.1109\/CVPR.2008.4587756"},{"key":"27_CR28","doi-asserted-by":"crossref","unstructured":"Wang, H., Ullah, M.M., Klaser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC 2009-British Machine Vision Conference, p. 124:1. BMVA Press (2009)","DOI":"10.5244\/C.23.124"},{"key":"27_CR29","doi-asserted-by":"crossref","unstructured":"Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization, pp. 1\u20138 (2007)","DOI":"10.1109\/CVPR.2007.383266"},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Tang, K., Fei-Fei, L., Koller, D.: Learning latent temporal structure for complex event detection, pp. 1250\u20131257 (2012)","DOI":"10.1109\/CVPR.2012.6247808"},{"key":"27_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-642-17711-8_29","volume-title":"Recognizing Patterns in Signals, Speech, Images and Videos","author":"R Vezzani","year":"2010","unstructured":"Vezzani, R., Baltieri, D., Cucchiara, R.: HMM based action recognition with projection histogram features. In: \u00dcnay, D., \u00c7ataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 286\u2013293. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-17711-8_29"},{"issue":"1","key":"27_CR32","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1023\/B:DAMI.0000005258.31418.83","volume":"8","author":"J Han","year":"2004","unstructured":"Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53\u201387 (2004)","journal-title":"Data Min. Knowl. Discov."},{"key":"27_CR33","doi-asserted-by":"crossref","unstructured":"Chang, H.-Y., Lin, J.-C., Cheng, M.-L., Huang, S.-C.: A novel incremental data mining algorithm based on FP-growth for big data. In: 2016 International Conference on Networking and Network Applications (NaNA), pp. 375\u2013378. IEEE (2016)","DOI":"10.1109\/NaNA.2016.77"},{"key":"27_CR34","unstructured":"Aditya, P.: Market basket analysis using FP-growth algorithm in organic medicine store. Skripsi, Fakultas Ilmu Komputer (2016)"},{"key":"27_CR35","doi-asserted-by":"crossref","unstructured":"Dharmaraajan, K., Dorairangaswamy, M.: Analysis of FP-growth and Apriori algorithms on pattern discovery from weblog data. In: IEEE International Conference on Advances in Computer Applications (ICACA), pp. 170\u2013174. IEEE (2016)","DOI":"10.1109\/ICACA.2016.7887945"},{"key":"27_CR36","unstructured":"Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)"},{"key":"27_CR37","doi-asserted-by":"crossref","unstructured":"Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition, pp. 2556\u20132563 (2011)","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"27_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-319-46466-4_50","volume-title":"Computer Vision \u2013 ECCV 2016","author":"G Lev","year":"2016","unstructured":"Lev, G., Sadeh, G., Klein, B., Wolf, L.: RNN fisher vectors for action recognition and image annotation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 833\u2013850. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_50"},{"key":"27_CR39","doi-asserted-by":"crossref","unstructured":"Duta, I.C., Ionescu, B., Aizawa, K., Sebe, N., et al.: Spatio-temporal vector of locally max pooled features for action recognition in videos. In: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), pp. 3205\u20133214. IEEE (2017)","DOI":"10.1109\/CVPR.2017.341"},{"key":"27_CR40","doi-asserted-by":"crossref","unstructured":"Lan, Z., Lin, M., Li, X., Hauptmann, A.G., Raj, B.: Beyond Gaussian pyramid: multi-skip feature stacking for action recognition, pp. 204\u2013212 (2015)","DOI":"10.1109\/CVPR.2015.7298616"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-03338-5_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T23:05:42Z","timestamp":1775257542000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-03338-5_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030033378","9783030033385"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-03338-5_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"3 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/prcv.qyhw.net.cn\/?lang=en&meeting_id=255","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}