{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T10:24:58Z","timestamp":1778840698121,"version":"3.51.4"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319105772","type":"print"},{"value":"9783319105789","type":"electronic"}],"license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-319-10578-9_44","type":"book-chapter","created":{"date-parts":[[2014,8,13]],"date-time":"2014-08-13T20:51:57Z","timestamp":1407963117000},"page":"675-688","source":"Crossref","is-referenced-by-count":21,"title":["Recognizing Complex Events in Videos by Learning Key Static-Dynamic Evidences"],"prefix":"10.1007","author":[{"given":"Kuan-Ting","family":"Lai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming-Syan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shih-Fu","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Agarwal, S.: The infinite push: A new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list. In: SDM, pp. 839\u2013850. Society for Industrial and Applied Mathematics (2011)","DOI":"10.1137\/1.9781611972818.72"},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Bhattacharya, S., Yu, F.X., Chang, S.F.: Minimally needed evidence for complex event recognition in unconstrained videos. In: ICMR (2014)","DOI":"10.1145\/2578726.2578740"},{"key":"44_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1007\/978-3-642-33709-3_49","volume-title":"Computer Vision \u2013 ECCV 2012","author":"L. Cao","year":"2012","unstructured":"Cao, L., Mu, Y., Natsev, A., Chang, S.-F., Hua, G., Smith, J.R.: Scene aligned pooling for complex video recognition. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol.\u00a07573, pp. 688\u2013701. Springer, Heidelberg (2012)"},{"issue":"12","key":"44_CR4","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1109\/TPAMI.2006.248","volume":"28","author":"Y. Chen","year":"2006","unstructured":"Chen, Y., Bi, J., Wang, J.Z.: Miles: Multiple-instance learning via embedded instance selection. PAMI\u00a028(12), 1931\u20131947 (2006)","journal-title":"PAMI"},{"issue":"1","key":"44_CR5","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S0004-3702(96)00034-3","volume":"89","author":"T.G. Dietterich","year":"1997","unstructured":"Dietterich, T.G., Lathrop, R.H., Lozano-P\u00e9rez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence\u00a089(1), 31\u201371 (1997)","journal-title":"Artificial Intelligence"},{"key":"44_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1007\/978-3-642-15549-9_36","volume-title":"Computer Vision \u2013 ECCV 2010","author":"N. Ikizler-Cinbis","year":"2010","unstructured":"Ikizler-Cinbis, N., Sclaroff, S.: Object, scene and actions: Combining multiple features for human action recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol.\u00a06311, pp. 494\u2013507. Springer, Heidelberg (2010)"},{"key":"44_CR7","unstructured":"INRIA: Yael library: Optimized implementations of computationally demanding functions (2009), \n                    \n                      https:\/\/gforge.inria.fr\/projects\/yael\/"},{"key":"44_CR8","unstructured":"Jiang, Y.G., Bhattacharya, S., Chang, S.F., Shah, M.: High-level event recognition in unconstrained videos. IJMIR, 1\u201329 (2012)"},{"key":"44_CR9","doi-asserted-by":"crossref","unstructured":"Joachims, T.: Optimizing search engines using clickthrough data. In: SIGKDD, pp. 133\u2013142. ACM (2002)","DOI":"10.1145\/775066.775067"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Li, W., Yu, Q., Divakaran, A., Vasconcelos, N.: Dynamic pooling for complex event recognition. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.339"},{"issue":"2","key":"44_CR11","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"D.G. Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV\u00a060(2), 91\u2013110 (2004)","journal-title":"IJCV"},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Natarajan, P., Wu, S., Vitaladevuni, S., Zhuang, X., Tsakalidis, S., Park, U., Prasad, R.: Multimodal feature fusion for robust event detection in web videos. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6247814"},{"key":"44_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/978-3-642-15552-9_29","volume-title":"Computer Vision \u2013 ECCV 2010","author":"J.C. Niebles","year":"2010","unstructured":"Niebles, J.C., Chen, C.-W., Fei-Fei, L.: Modeling temporal structure of decomposable motion segments for activity classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol.\u00a06312, pp. 392\u2013405. Springer, Heidelberg (2010)"},{"key":"44_CR14","doi-asserted-by":"crossref","unstructured":"Oneata, D., Verbeek, J., Schmid, C.: Action and event recognition with fisher vectors on a compact feature set. In: ICCV, pp. 1817\u20131824 (2013)","DOI":"10.1109\/ICCV.2013.228"},{"key":"44_CR15","unstructured":"Over, P., Awad, G., Michel, M., Fiscus, J., Sanders, G., Kraaij, W., Smeaton, A.F., Quenot, G.: Trecvid 2013 \u2013 an overview of the goals, tasks, data, evaluation mechanisms and metrics. In: Proceedings of TRECVID 2013. NIST (2013)"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Quattoni, A., Carreras, X., Collins, M., Darrell, T.: An efficient projection for l\n                  1,\u2009\u221e\u2009, infinity regularization. In: ICML (2009)","DOI":"10.1145\/1553374.1553484"},{"key":"44_CR17","unstructured":"Rakotomamonjy, A.: Sparse support vector infinite push. In: ICML (2012)"},{"key":"44_CR18","first-page":"2233","volume":"10","author":"C. Rudin","year":"2009","unstructured":"Rudin, C.: The p-norm push: A simple convex ranking algorithm that concentrates at the top of the list. JMLR\u00a010, 2233\u20132271 (2009)","journal-title":"JMLR"},{"key":"44_CR19","unstructured":"Soomro, K., Zamir, A.R., Shah, M.: Ucf101: A dataset of 101 human actions classes from videos in the wild. CRCV-TR-12-01 (2012)"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Tamrakar, A., Ali, S., Yu, Q., Liu, J., Javed, O., Divakaran, A., Cheng, H., Sawhney, H.: Evaluation of low-level features and their combinations for complex event detection in open source videos. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6248114"},{"key":"44_CR21","doi-asserted-by":"crossref","unstructured":"Tang, K., Fei-Fei, L., Koller, D.: Learning latent temporal structure for complex event detection. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6247808"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Vahdat, A., Cannons, K., Mori, G., Oh, S., Kim, I.: Compositional models for video event detection: A multiple kernel learning latent variable approach. In: ICCV, pp. 1185\u20131192 (2013)","DOI":"10.1109\/ICCV.2013.463"},{"key":"44_CR23","unstructured":"Vedaldi, A., Fulkerson, B.: Vlfeat: An open and portable library of computer vision algorithms (2008), \n                    \n                      http:\/\/www.vlfeat.org\/"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Wang, H., Klaser, A., Schmid, C., Liu, C.L.: Action recognition by dense trajectories. In: CVPR (2011)","DOI":"10.1109\/CVPR.2011.5995407"},{"key":"44_CR25","doi-asserted-by":"crossref","unstructured":"Wang, H., Kl\u00e4ser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. IJCV, 1\u201320 (2013)","DOI":"10.1007\/s11263-012-0594-8"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2014"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-10578-9_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T10:06:36Z","timestamp":1575281196000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-10578-9_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319105772","9783319105789"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-10578-9_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014]]}}}