Sequential Monte Carlo filter based on multiple strategies for a scene specialization classifier
Transfer learning approaches have shown interesting results by using knowledge from source domains to learn a specialized classifier/detector for a target domain containing unlabeled data or only a few labeled samples. In this paper, we present a new transductive transfer learning framework based on...
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| Published in | EURASIP journal on image and video processing Vol. 2016; no. 1; pp. 1 - 19 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
01.12.2016
Springer Nature B.V Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-5281 1687-5176 1687-5281 |
| DOI | 10.1186/s13640-016-0143-4 |
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| Abstract | Transfer learning approaches have shown interesting results by using knowledge from source domains to learn a specialized classifier/detector for a target domain containing unlabeled data or only a few labeled samples. In this paper, we present a new transductive transfer learning framework based on a sequential Monte Carlo filter to specialize a generic classifier towards a specific scene. The proposed framework utilizes different strategies and approximates iteratively the hidden target distribution as a set of samples in order to learn a specialized classifier. These training samples are selected from both source and target domains according to their weight importance, which indicates that they belong to the target distribution. The resulting classifier is applied to pedestrian and car detection on several challenging traffic scenes. The experiments have demonstrated that our solution improves and outperforms several state of the art’s specialization algorithms on public datasets. |
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| AbstractList | Transfer learning approaches have shown interesting results by using knowledge from source domains to learn a specialized classifier/detector for a target domain containing unlabeled data or only a few labeled samples. In this paper, we present a new transductive transfer learning framework based on a sequential Monte Carlo filter to specialize a generic classifier towards a specific scene. The proposed framework utilizes different strategies and approximates iteratively the hidden target distribution as a set of samples in order to learn a specialized classifier. These training samples are selected from both source and target domains according to their weight importance, which indicates that they belong to the target distribution. The resulting classifier is applied to pedestrian and car detection on several challenging traffic scenes. The experiments have demonstrated that our solution improves and outperforms several state of the art’s specialization algorithms on public datasets. |
| ArticleNumber | 40 |
| Author | Ben Amara, Najoua Essoukri Maâmatou, Houda Goyat, Yann Gazzah, Sami Chateau, Thierry |
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| CitedBy_id | crossref_primary_10_14778_3137628_3137664 crossref_primary_10_1007_s13735_019_00180_z crossref_primary_10_1109_TIP_2017_2779271 crossref_primary_10_1186_s13640_016_0154_1 crossref_primary_10_1109_TITS_2018_2876614 |
| Cites_doi | 10.1007/978-3-319-10578-9_31 10.1023/A:1008078328650 10.1007/978-1-4757-3437-9 10.1109/CVPR.2014.222 10.1109/CVPR.1994.323794 10.1109/ICCV.2011.6126504 10.1109/TPAMI.2010.117 10.1109/TPAMI.2011.66 10.1109/ICCV.2003.1238406 10.1109/CVPR.2008.4587597 10.1109/TPAMI.2013.124 10.5244/C.24.68 10.5220/0003822105130520 10.1109/CVPR.2010.5540064 10.1109/CVPR.2010.5539906 10.1109/CVPR.2011.5995698 10.1007/s11263-007-0067-7 10.1109/ITSC.2010.5624980 10.1109/WACV.2015.30 10.5220/0005725104110422 10.1007/s11263-009-0275-4 10.1109/TPAMI.2004.108 10.1109/CVPR.2004.1315181 10.1109/CVPR.2005.177 10.1109/CVPR.2012.6248064 10.1109/CVPR.2011.5995403 10.5244/C.23.91 10.1109/ISBI.2007.357035 10.1109/ACVMOT.2005.107 10.1109/TPAMI.2014.2300479 10.1109/WISP.2015.7139161 10.1109/TPAMI.2008.87 |
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| Keywords | Sample-proposal and observation strategies Sequential Monte Carlo filter Specialization Transductive transfer learning Generic and specialized classifier |
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| References_xml | – reference: V Nair, JJ Clark, in Computer Vision and Pattern Recognition (CVPR), Proceedings of the 2004 IEEE Conference on, 2. An unsupervised, online learning framework for moving object detection (IEEE, 2004), pp. II–317. – reference: C Rosenberg, M Hebert, H Schneiderman, in Application of Computer Vision, 2005. WACV/MOTIONS ’05 Volume 1. Seventh IEEE Workshops on. Semi-supervised self-training of object detection models (IEEE Press, 2005), pp. 29–36. – reference: IsardMBlakeACondensation—conditional density propagation for visual trackingIJCV199829152810.1023/A:1008078328650 – reference: S Alvarez, M Sotelo, I Parra, D Llorca, M Gavilán, in Proceedings of the World Congress on Engineering and Computer Science (WCECS), 2. Vehicle and pedestrian detection in esafety applications, (2009), pp. 1–6. – reference: M Wang, W Li, X Wang, in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. Transferring a generic pedestrian detector towards specific scenes (IEEE, 2012), pp. 3274–3281. – reference: LinB-FChanY-MFuL-CHsiaoP-YChuangL-AHuangS-SLoM-FIntegrating appearance and edge features for sedan vehicle detection in the blind-spot areaITS2012132737747 – reference: PanSJTsangIWKwokJTYangQDomain adaptation via transfer component analysisNN2011222199210 – reference: CarbonettoPDorko, ǴSchmidCKückHDe FreitasNLearning to recognize objects with little supervisionIJCV2008771-321923710.1007/s11263-007-0067-7 – reference: K Ali, D Hasler, F Fleuret, in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. Flowboost–appearance learning from sparsely annotated video (IEEE, 2011), pp. 1433–1440. – reference: P Felzenszwalb, D McAllester, D Ramanan, in Computer Vision and Pattern Recognition, CVPR 2008, IEEE Conference on. A discriminatively trained, multiscale, deformable part model (IEEE, 2008), pp. 1–8. – reference: K Tang, V Ramanathan, L Fei-Fei, D Koller, in Advances in Neural Information Processing Systems (NIPS). Shifting weights: adapting object detectors from image to video, (2012), pp. 638–646. – reference: DoucetADe FreitasNGordonNSequential Monte Carlo methods in practice2001New YorkSpringer Science + Business Media10.1007/978-1-4757-3437-90967.00022 – reference: BoltzSDebreuveEBarlaudMHigh-dimensional statistical measure for region-of-interest trackingIP2009186126612832742157 – reference: YuanQThangaliAAblavskyVSclaroffSLearning a family of detectors via multiplicative kernelsPAMI201133351453010.1109/TPAMI.2010.117 – reference: T Chesnais, N Allezard, Y Dhome, T Chateau, in VISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications, Volume 1. Automatic process to build a contextualized detector (SciTePress, 2012), pp. 513–520. – reference: MeiXLingHRobust visual tracking and vehicle classification via sparse representationPAMI201133112259227210.1109/TPAMI.2011.66 – reference: T Tommasi, F Orabona, B Caputo, in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. Safety in numbers: learning categories from few examples with multi model knowledge transfer (IEEE, 2010), pp. 3081–3088. – reference: WangXMaXGrimsonWELUnsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian modelsPAMI200931353955510.1109/TPAMI.2008.87 – reference: D Sun, J Watada, in Intelligent Signal Processing (WISP), 9th International Symposium on. Detecting pedestrians and vehicles in traffic scene based on boosted HOG features and SVM (IEEE, 2015), pp. 1–4. – reference: H Maâmatou, T Chateau, S Gazzah, Y Goyat, N Essoukri Ben Amara, in Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP. Transductive transfer learning to specialize a generic classifier towards a specific scene (SciTePress, 2016), pp. 411–422. – reference: SivaramanSTrivediMMVehicle detection by independent parts for urban driver assistanceITS201314415971608 – reference: QuanzBHuanJMishraMKnowledge transfer with low-quality data: a feature extraction issueKDE2012241017891802 – reference: F Han, Y Shan, R Cekander, HS Sawhney, R Kumar, in Performance Metrics for Intelligent Systems (PMIS) 2006 Workshop. A two-stage approach to people and vehicle detection with hog-based SVM (Citeseer, 2006), pp. 133–140. – reference: X Zhang, N Zheng, in Intelligent Transportation Systems (ITSC), 13th International IEEE Conference on. Vehicle detection under varying poses using conditional random fields (IEEE, 2010), pp. 875–880. – reference: J Shi, C Tomasi, in Computer Vision and Pattern Recognition (CVPR), 1994 IEEE Conference on. Good features to track (IEEE, 1994), pp. 593–600. – reference: DanescuROnigaFNedevschiSModeling and tracking the driving environment with a particle-based occupancy gridITS201112413311342 – reference: M Wang, X Wang, in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. Automatic adaptation of a generic pedestrian detector to a specific traffic scene (IEEE, 2011), pp. 3401–3408. – reference: I Smal, W Niessen, E Meijering, in 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 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| Title | Sequential Monte Carlo filter based on multiple strategies for a scene specialization classifier |
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