{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T18:47:46Z","timestamp":1785955666748,"version":"3.56.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,2,12]],"date-time":"2022-02-12T00:00:00Z","timestamp":1644624000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,12]],"date-time":"2022-02-12T00:00:00Z","timestamp":1644624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s10489-021-03068-w","type":"journal-article","created":{"date-parts":[[2022,2,12]],"date-time":"2022-02-12T12:02:22Z","timestamp":1644667342000},"page":"12771-12787","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Hybrid handcrafted and learned feature framework for human action recognition"],"prefix":"10.1007","volume":"52","author":[{"given":"Chaolong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanping","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,12]]},"reference":[{"issue":"3","key":"3068_CR1","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1016\/j.patcog.2012.07.024","volume":"46","author":"A Bolovinou","year":"2013","unstructured":"Bolovinou A, Pratikakis I, Perantonis S (2013) Bag of spatio-visual words for context inference in scene classification. Pattern Recognition 46(3):1039\u20131053. https:\/\/doi.org\/10.1016\/j.patcog.2012.07.024","journal-title":"Pattern Recognition"},{"key":"3068_CR2","doi-asserted-by":"publisher","unstructured":"Chandra MA, Bedi SS (2018) Survey on SVM and their application in image classification. International Journal of Information Technology pp 1\u201311. https:\/\/doi.org\/10.1007\/s41870-017-0080-1","DOI":"10.1007\/s41870-017-0080-1"},{"key":"3068_CR3","doi-asserted-by":"crossref","unstructured":"Chang J, Wang L, Meng G, Xiang S, Pan C (2017) Deep Adaptive Image Clustering. In: International Conference on Computer Vision. IEEE, pp 5880\u20135888. https:\/\/doi.org\/10.1109\/ICCV.2017.626","DOI":"10.1109\/ICCV.2017.626"},{"key":"3068_CR4","doi-asserted-by":"crossref","unstructured":"Gammulle H, Denman S, Sridharan S, Fookes C (2017) Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp 177\u2013186. IEEE. https:\/\/doi.org\/10.1109\/WACV.2017.27","DOI":"10.1109\/WACV.2017.27"},{"issue":"1","key":"3068_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 (2013) 3D Convolutional Neural Networks for Human Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1):221\u2013231. https:\/\/doi.org\/10.1109\/TPAMI.2012.59","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"3068_CR6","doi-asserted-by":"crossref","unstructured":"Jiang J, Deng C, Cheng X (2017) Action prediction based on dense trajectory and dynamic image. In: Chinese Automation Congress. IEEE, pp 1175\u20131180 https:\/\/doi.org\/10.1109\/CAC.2017.8242944","DOI":"10.1109\/CAC.2017.8242944"},{"key":"3068_CR7","doi-asserted-by":"crossref","unstructured":"Jin S, Su H, Stauffer C, LearnedMiller E (2017) End-to-End Face Detection and Cast Grouping in Movies Using Erd\u00f6s-R\u00e9nyi Clustering. In: International Conference on Computer Vision. IEEE, pp 5286\u20135295 https:\/\/doi.org\/10.1109\/ICCV.2017.564","DOI":"10.1109\/ICCV.2017.564"},{"key":"3068_CR8","doi-asserted-by":"crossref","unstructured":"Ju S, Xiao W, Shuicheng Y, LoongFah C, Tat-Seng C, Jintao L (2009) Hierarchical spatio-temporal context modeling for action recognition. In: Conference on Computer Vision and Pattern Recognition. IEEE, pp 2004\u20132011 https:\/\/doi.org\/10.1109\/CVPRW.2009.5206721","DOI":"10.1109\/CVPRW.2009.5206721"},{"key":"3068_CR9","doi-asserted-by":"crossref","unstructured":"Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L(2014) Large-Scale Video Classification with Convolutional Neural Networks. In: Computer Vision and Pattern Recognition. IEEE, pp 1725\u20131732 https:\/\/doi.org\/10.1109\/CVPR.2014.223","DOI":"10.1109\/CVPR.2014.223"},{"key":"3068_CR10","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.eswa.2017.05.021","volume":"85","author":"T Kieu","year":"2017","unstructured":"Kieu T, Vo B, Le T, Deng ZH, Le B (2017) B: Mining top-k co-occurrence items with sequential pattern. Expert Systems with Applications 85:123\u2013133. https:\/\/doi.org\/10.1016\/j.eswa.2017.05.021","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"3068_CR11","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Communications of the ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Communications of the ACM"},{"key":"3068_CR12","doi-asserted-by":"crossref","unstructured":"Kuehne H, Jhuang H (2011) HMDB: A large video database for human motion recognition. In: International Conference on Computer Vision. IEEE, pp 2556\u20132563 https:\/\/doi.org\/10.1109\/ICCV.2011.6126543","DOI":"10.1109\/ICCV.2011.6126543"},{"issue":"2","key":"3068_CR13","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11263-005-1838-7","volume":"64","author":"I Laptev","year":"2005","unstructured":"Laptev I (2005) On Space-Time Interest Points. International Journal of Computer Vision 64(2):107\u2013123. https:\/\/doi.org\/10.1007\/s11263-005-1838-7","journal-title":"International Journal of Computer Vision"},{"key":"3068_CR14","doi-asserted-by":"crossref","unstructured":"Laptev I, Marszalek M, Schmid C, Rozenfeld B (2008) Learning realistic human actions from movies. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 1\u20138 https:\/\/doi.org\/10.1109\/CVPR.2008.4587756","DOI":"10.1109\/CVPR.2008.4587756"},{"key":"3068_CR15","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.patcog.2018.08.006","volume":"85","author":"DG Lee","year":"2019","unstructured":"Lee DG, Lee SW (2019) Prediction of partially observed human activity based on pre-trained deep representation. Pattern Recognition 85:198\u2013206. https:\/\/doi.org\/10.1016\/j.patcog.2018.08.006","journal-title":"Pattern Recognition"},{"key":"3068_CR16","doi-asserted-by":"publisher","unstructured":"Li W, Wen L, Chang M, Lim SN, Lyu S (2017) Adaptive RNN Tree for Large-Scale Human Action Recognition. In: International Conference on Computer Vision. IEEE, pp 1453\u20131461 https:\/\/doi.org\/10.1109\/ICCV.2017.161","DOI":"10.1109\/ICCV.2017.161"},{"key":"3068_CR17","doi-asserted-by":"publisher","unstructured":"Liu J, Jiebo Luo, Shah M (2009) Recognizing realistic actions from videos \u201cin the wild\u201d. In: Conference on Computer Vision and Pattern Recognition. IEEE, pp 1996\u20132003 https:\/\/doi.org\/10.1109\/CVPR.2009.5206744","DOI":"10.1109\/CVPR.2009.5206744"},{"key":"3068_CR18","doi-asserted-by":"crossref","unstructured":"Liu P, Wang J, She M, Liu H (2011) Human action recognition based on 3D SIFT and LDA model. In: Workshop on Robotic Intelligence In Informationally Structured Space, pp 12\u201317 https:\/\/doi.org\/10.1109\/RIISS.2011.5945790","DOI":"10.1109\/RIISS.2011.5945790"},{"key":"3068_CR19","doi-asserted-by":"publisher","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C, Berg AC (2016) SSD: Single Shot MultiBox Detector. In: European Conference on Computer Vision, pp 21\u201337 https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"1","key":"3068_CR20","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s11042-017-5251-3","volume":"78","author":"X Lu","year":"2019","unstructured":"Lu X, Yao H, Zhao S, Sun X, Zhang S (2019) Action recognition with multi-scale trajectory-pooled 3D convolutional descriptors. Multimedia Tools and Applications 78(1):507\u2013523. https:\/\/doi.org\/10.1007\/s11042-017-5251-3","journal-title":"Multimedia Tools and Applications"},{"key":"3068_CR21","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.neucom.2018.10.095","volume":"396","author":"M Majd","year":"2020","unstructured":"Majd M, Safabakhsh R (2020) Correlational Convolutional LSTM for human action recognition. Neurocomputing 396:224\u2013229. https:\/\/doi.org\/10.1016\/j.neucom.2018.10.095","journal-title":"Neurocomputing"},{"key":"3068_CR22","doi-asserted-by":"publisher","unstructured":"Messing R, Pal C, Kautz H (2009) Activity recognition using the velocity histories of tracked keypoints. In: International Conference on Computer Vision. IEEE, pp 104\u2013111 https:\/\/doi.org\/10.1109\/ICCV.2009.5459154","DOI":"10.1109\/ICCV.2009.5459154"},{"key":"3068_CR23","doi-asserted-by":"publisher","unstructured":"Murtaza F, HaroonYousaf M, Velastin SA (2018) DA-VLAD: Discriminative Action Vector of Locally Aggregated Descriptors for Action Recognition. In: IEEE International Conference on Image Processing (ICIP). IEEE, pp 3993\u20133997 https:\/\/doi.org\/10.1109\/ICIP.2018.8451255","DOI":"10.1109\/ICIP.2018.8451255"},{"key":"3068_CR24","doi-asserted-by":"publisher","unstructured":"Peng X, Zou C, Qiao Y, Peng Q (2014) Action Recognition with Stacked Fisher Vectors. In: European Conference on Computer Vision. Springer, pp 581\u2013595 https:\/\/doi.org\/10.1007\/978-3-319-10602-1_38","DOI":"10.1007\/978-3-319-10602-1_38"},{"key":"3068_CR25","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You Only Look Once: Unified, Real-Time Object Detection. In: Computer Vision and Pattern Recognition. IEEE, pp 779\u2013788 https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"3068_CR26","doi-asserted-by":"publisher","unstructured":"Ryoo MS (2011) Human activity prediction: Early recognition of ongoing activities from streaming videos. In: International Conference on Computer Vision. IEEE, pp 1036\u20131043 https:\/\/doi.org\/10.1109\/ICCV.2011.6126349","DOI":"10.1109\/ICCV.2011.6126349"},{"key":"3068_CR27","doi-asserted-by":"crossref","unstructured":"Ryoo MS, Aggarwal JK (2010) UT-Interaction Dataset, ICPR contest on Semantic Description of Human Activities (SDHA). https:\/\/cvrc.ece.utexas.edu\/SDHA2010\/Human_Interaction.html","DOI":"10.1007\/978-3-642-17711-8_28"},{"issue":"1","key":"3068_CR28","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3390\/app7010110","volume":"7","author":"A Sargano","year":"2017","unstructured":"Sargano A, Angelov P, Habib Z (2017) A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition. Applied Sciences 7(1):110. https:\/\/doi.org\/10.3390\/app7010110","journal-title":"Applied Sciences"},{"key":"3068_CR29","unstructured":"Simonyan K, Zisserman A (2014) Two-Stream Convolutional Networks for Action Recognition in Videos. In: Advances in neural information processing systems, pp 568\u2013576"},{"key":"3068_CR30","unstructured":"Simonyan K, Zisserman A (2015) Very Deep Convolutional Networks for Large-Scale Image Recognition. In: International Conference on Learning Representations, pp 769\u2013784"},{"key":"3068_CR31","doi-asserted-by":"publisher","unstructured":"Sipiran I, Bustos B (2011) Harris 3D: A robust extension of the Harris operator for interest point detection on 3D meshes. In: Visual Computer, p. 963\u2013976 https:\/\/doi.org\/10.1007\/s00371-011-0610-y","DOI":"10.1007\/s00371-011-0610-y"},{"key":"3068_CR32","volume-title":"PyTorch: An Imperative Style","author":"B Steiner","year":"2019","unstructured":"Steiner B, DeVito Z, Chintala S, Gross S, Paszke A, Massa F, Lerer A, Chanan G, Lin Z, Yang E, Desmaison A, Tejani A, Kopf A, Bradbury J, Antiga L, Raison M, Gimelshein N, Chilamkurthy S, Killeen T, Fang L, Bai J (2019) PyTorch: An Imperative Style. Advances in Neural Information Processing Systems (NIPS), High-Performance Deep Learning Library. In"},{"key":"3068_CR33","doi-asserted-by":"publisher","unstructured":"Sun J, Mu Y, Yan S, Cheong LF (2010) Activity recognition using dense long-duration trajectories. In: International Conference on Multimedia and Expo. IEEE, pp 322\u2013327 https:\/\/doi.org\/10.1109\/ICME.2010.5583046","DOI":"10.1109\/ICME.2010.5583046"},{"issue":"2","key":"3068_CR34","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1111\/j.1467-8659.2012.03013.x","volume":"31","author":"M Tao","year":"2012","unstructured":"Tao M, Bai J, Kohli P, Paris S (2012) SimpleFlow: A non-iterative, sublinear optical flow algorithm. Computer Graphics Forum 31(2):345\u2013353. https:\/\/doi.org\/10.1111\/j.1467-8659.2012.03013.x","journal-title":"Computer Graphics Forum"},{"key":"3068_CR35","doi-asserted-by":"publisher","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning Spatiotemporal Features with 3D Convolutional Networks. In: International Conference on Computer Vision, 1. IEEE, pp 4489\u20134497 https:\/\/doi.org\/10.1109\/ICCV.2015.510","DOI":"10.1109\/ICCV.2015.510"},{"key":"3068_CR36","doi-asserted-by":"crossref","unstructured":"Van Droogenbroeck M, Barnich O (2014) ViBe: A Disruptive Method for Background Subtraction. In: Background Modeling and Foreground Detection for Video Surveillance. Chapman and Hall\/CRC, pp 7.1\u20137.23 https:\/\/doi.org\/10.1201\/b17223-10","DOI":"10.1201\/b17223-10"},{"issue":"6","key":"3068_CR37","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/TPAMI.2017.2712608","volume":"40","author":"G Varol","year":"2018","unstructured":"Varol G, Laptev I, Schmid C (2018) Long-Term Temporal Convolutions for Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 40(6):1510\u20131517. https:\/\/doi.org\/10.1109\/TPAMI.2017.2712608","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"20","key":"3068_CR38","doi-asserted-by":"publisher","first-page":"6957","DOI":"10.1016\/j.eswa.2015.04.039","volume":"42","author":"DK Vishwakarma","year":"2015","unstructured":"Vishwakarma DK, Kapoor R (2015) Hybrid classifier based human activity recognition using the silhouette and cells. Expert Systems with Applications 42(20):6957\u20136965. https:\/\/doi.org\/10.1016\/j.eswa.2015.04.039","journal-title":"Expert Systems with Applications"},{"key":"3068_CR39","doi-asserted-by":"publisher","first-page":"85284","DOI":"10.1109\/ACCESS.2020.2993227","volume":"8","author":"Y Wan","year":"2020","unstructured":"Wan Y, Yu Z, Wang Y, Li X (2020) Action Recognition Based on Two-Stream Convolutional Networks with Long-Short-Term Spatiotemporal Features. IEEE Access 8:85284\u201385293. https:\/\/doi.org\/10.1109\/ACCESS.2020.2993227","journal-title":"IEEE Access"},{"issue":"1","key":"3068_CR40","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","volume":"103","author":"H Wang","year":"2013","unstructured":"Wang H, Kl\u00e4ser A, Schmid C, Liu C (2013) Dense Trajectories and Motion Boundary Descriptors for Action Recognition. International Journal of Computer Vision 103(1):60\u201379. https:\/\/doi.org\/10.1007\/s11263-012-0594-8","journal-title":"International Journal of Computer Vision"},{"key":"3068_CR41","doi-asserted-by":"publisher","unstructured":"Wang H, Schmid C (2013) Action Recognition with Improved Trajectories. In: International Conference on Computer Vision. IEEE, pp 3551\u20133558 https:\/\/doi.org\/10.1109\/ICCV.2013.441","DOI":"10.1109\/ICCV.2013.441"},{"issue":"8","key":"3068_CR42","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1016\/j.sigpro.2012.06.009","volume":"93","author":"J Wang","year":"2013","unstructured":"Wang J, Xu Z (2013) STV-based video feature processing for action recognition. Signal Processing 93(8):2151\u20132168. https:\/\/doi.org\/10.1016\/j.sigpro.2012.06.009","journal-title":"Signal Processing"},{"key":"3068_CR43","doi-asserted-by":"publisher","unstructured":"Wang L, Qiao Y, Tang X (2015) Action recognition with trajectory-pooled deep-convolutional descriptors. In: Computer Society Conference on Computer Vision and Pattern Recognition, pp 4305\u20134314 https:\/\/doi.org\/10.1109\/CVPR.2015.7299059","DOI":"10.1109\/CVPR.2015.7299059"},{"key":"3068_CR44","doi-asserted-by":"publisher","unstructured":"Wang Y, Long M, Wang J, Yu PS (2017) Spatiotemporal Pyramid Network for Video Action Recognition. In: Computer Vision and Pattern Recognition (CVPR). IEEE, pp 2097\u20132106 https:\/\/doi.org\/10.1109\/CVPR.2017.226","DOI":"10.1109\/CVPR.2017.226"},{"key":"3068_CR45","unstructured":"Wu G, Mahoor MH, Althloothi S, Voyles RM (2010) SIFT-Motion Estimation (SIFT-ME): A New Feature for Human Activity Recognition. In: IPCV, pp 804\u2013811"},{"key":"3068_CR46","doi-asserted-by":"publisher","unstructured":"Wu W, Kan M, Liu X, Yang Y, Shan S, Chen X (2017) Recursive Spatial Transformer (ReST) for Alignment-Free Face Recognition. In: International Conference on Computer Vision. IEEE, pp 3792\u20133800 https:\/\/doi.org\/10.1109\/ICCV.2017.407","DOI":"10.1109\/ICCV.2017.407"},{"key":"3068_CR47","doi-asserted-by":"publisher","first-page":"109226","DOI":"10.1016\/j.measurement.2021.109226","volume":"176","author":"F Xue","year":"2021","unstructured":"Xue F, Zhang W, Xue F, Li D, Xie S, Fleischer J (2021) A novel intelligent fault diagnosis method of rolling bearing based on two-stream feature fusion convolutional neural network. Measurement 176:109226. https:\/\/doi.org\/10.1016\/j.measurement.2021.109226","journal-title":"Measurement"},{"issue":"6","key":"3068_CR48","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1007\/s10489-018-1347-3","volume":"49","author":"G Yao","year":"2019","unstructured":"Yao G, Lei T, Zhong J, Jiang P (2019) Learning multi-temporal-scale deep information for action recognition. Applied Intelligence 49(6):2017\u20132029. https:\/\/doi.org\/10.1007\/s10489-018-1347-3","journal-title":"Applied Intelligence"},{"key":"3068_CR49","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS\u201914. MIT Press, Cambridge, MA, USA, p 3320\u20133328"},{"key":"3068_CR50","doi-asserted-by":"publisher","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European Conference on Computer Vision. Springer, pp 818\u2013833 https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53","DOI":"10.1007\/978-3-319-10590-1_53"},{"issue":"5","key":"3068_CR51","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.3390\/s19051005","volume":"19","author":"HB Zhang","year":"2019","unstructured":"Zhang HB, Zhang YX, Zhong B, Lei Q, Yang L, Du JX, Chen DS (2019) A Comprehensive Survey of Vision-Based Human Action Recognition Methods. Sensors 19(5):1005. https:\/\/doi.org\/10.3390\/s19051005","journal-title":"Sensors"},{"key":"3068_CR52","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.patcog.2019.01.027","volume":"90","author":"J Zhang","year":"2019","unstructured":"Zhang J, Hu H (2019) Domain learning joint with semantic adaptation for human action recognition. Pattern Recognition 90:196\u2013209. https:\/\/doi.org\/10.1016\/j.patcog.2019.01.027","journal-title":"Pattern Recognition"},{"key":"3068_CR53","doi-asserted-by":"publisher","unstructured":"Zhao J, Snoek CGM (2019) Dance With Flow: Two-In-One Stream Action Detection. In: Conference on Computer Vision and Pattern Recognition. IEEE, pp 9927\u20139936 https:\/\/doi.org\/10.1109\/CVPR.2019.01017","DOI":"10.1109\/CVPR.2019.01017"},{"issue":"6","key":"3068_CR54","doi-asserted-by":"publisher","first-page":"2296","DOI":"10.1080\/01431161.2014.890762","volume":"35","author":"L Zhao","year":"2014","unstructured":"Zhao L, Tang P, Huo L (2014) A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification. International Journal of Remote Sensing 35(6):2296\u20132310. https:\/\/doi.org\/10.1080\/01431161.2014.890762","journal-title":"International Journal of Remote Sensing"},{"issue":"12","key":"3068_CR55","doi-asserted-by":"publisher","first-page":"4620","DOI":"10.1109\/JSTARS.2014.2339842","volume":"7","author":"LJ Zhao","year":"2014","unstructured":"Zhao LJ, Tang P, Huo LZ (2014) Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(12):4620\u20134631. https:\/\/doi.org\/10.1109\/JSTARS.2014.2339842","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"3068_CR56","doi-asserted-by":"publisher","unstructured":"Zhu Y, Lan Z, Newsam S, Hauptmann A (2019) Hidden Two-Stream Convolutional Networks for Action Recognition. In: Asian Conference on Computer Vision, pp 363\u2013378 https:\/\/doi.org\/10.1007\/978-3-030-20893-6_23","DOI":"10.1007\/978-3-030-20893-6_23"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03068-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-03068-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03068-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T12:26:13Z","timestamp":1663331173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-03068-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,12]]},"references-count":56,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["3068"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-03068-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,12]]},"assertion":[{"value":"30 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}