nach oben

Cognitive Computation

Erschienen in:

01.09.2014

Multitask Extreme Learning Machine for Visual Tracking

verfasst von: Huaping Liu, Fuchun Sun, Yuanlong Yu

Erschienen in: Cognitive Computation | Ausgabe 3/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, we try to address the joint optimization problem of the extreme learning machines corresponding to different features. The method is based on the L _2,1 norm penalty, which encourages joint sparse coding. By adopting such a technology, the intrinsic relation between different features can be sufficiently preserved. To tackle the problem that the labeled samples is rare, we introduce the semi-supervised regularization term and seamlessly incorporate them into the particle filter framework to realize visual tracking. In addition, an online updating strategy is introduced which also exploits the large amount of unlabeled samples that are collected during the tracking period. Finally, the proposed tracking algorithm is compared to other state-of-the-arts on some challenging video sequences and shows promising results.

Vorheriger Artikel An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels

Nächster Artikel Fast Image Recognition Based on Independent Component Analysis and Extreme Learning Machine

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Taylor JG. Cognitive computation. Cogn Comput. 2009;1(1):4–16.CrossRef

Underwood G. Cognitive processes in eye guidance: algorithms for attention in image processing. Cogn Comput. 2009;1(1):64–76.CrossRef

Wollmer M, Eyben F, Graves A, Schuller B, Rigoll G. Bidirectional LSTM networks for context-sensitive keyword detection in a cognitive virtual agent framework. Cogn Comput. 2010;2(3):180–190.CrossRef

Mital P, Smith T, Hill R, Henderson J. Clustering of gaze during dynamic scene viewing is predicted by motion. Cogn Comput. 2011;3(1):5–24.CrossRef

Cambria E, Hussain A. Sentic album: content-, concept-, and context-based online personal photo management system. Cogn Comput. 2012;4(4):477–496.CrossRef

Wang QF, Cambria E, Liu CL, Hussain A. Common sense knowledge for handwritten Chinese recognition. Cogn Comput. 2013;5(2):234–242.CrossRef

He B, Xu D, Nian R, van Heeswijk M, Yu Q, Miche Y, Lendasse A Fast face recognition via sparse coding and extreme learning machine. Cogn Comput. 2013. doi:10.1007/s12559-013-9224-1.

Yao Y, Guo P, Xin X, Jiang Z. Image fusion by hierarchical joint sparse representation. Cogn Comput. 2013;5(4).

Yang H, Shao L, Zheng F, Wang L, Song Z. Recent advances and trends in visual tracking: a review. Neurocomputing. 2011;74(18):3823-3831.CrossRef

10.

Yilmaz A, Javed O, Shah M. Object tracking: a survey. ACM Comput Surv. 2006;38:1–45.CrossRef

11.

Isard M, Blake A. CONDENSATION: Conditional density propagation for visual tracking. Int J of Comput V. 1998;29 (1):5–28.

12.

Liu H, Sun F. Semi-supervised particle filter for visual tracking, In: Proceeding of International Conference on Robotics and Automation (ICRA), 2009; p. 3928–3933.

13.

Avidan S. Support vector tracking. IEEE Trans Pattern Anal Mach Intell (PAMI). 2004;26(8):1064–1072.CrossRef

14.

Collins R, Liu Y, Leordeanu M. Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell (PAMI). 2005;27(10):1631–1643.CrossRef

15.

Avidan S. Ensemble tracking. IEEE Trans Pattern Anal Mach Intell (PAMI). 2007;29(2):261–271.CrossRef

16.

Grabner H, Bischof H. On-line boosting and vision, In: Proceedings of Computer Vision and Pattern Recognition (CVPR), 2006; p.260–267.

17.

Zhu X. Semi-supervised learning literature survey, Technical Report, Computer Sciences, University of Wisconsin-Madison; 2005.

18.

Tang F, Brennan S, Zhao Q, Tao H. Co-tracking using semi-supervised support vector machines, In: Proceedings of International Conference on Computer Vision (ICCV), 2007;p.1–8.

19.

Grabner H, Leistner C, Bischof H. Semi-supervised online boosting for robust tracking, In: European Conference on Computer Vision (ECCV), 2008.

20.

Babenko B, Yang MH, Belongie S. Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell. 2011;33:1619–1632.CrossRef

21.

Liu R, Cheng J, Lu H. A robust boosting tracker with minimum error bound in a co-training framework, In: Proceedings International Conference on Computer Vision(ICCV), 2009;p.1459–1466.

22.

Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing. 2006;70:489–501.CrossRef

23.

Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Sys Man Cybern Part B. 2012;42(2):513–529.CrossRef

24.

Savitha R, Suresh S, Kim HJ. A meta-cognitive learning algorithm for an extreme learning machine classifier. Cogn Comput. (in press).

25.

Zong W, Huang GB. Face recognition based on extreme learning machine. Neurocomputing. 2011;74:2541–2551.CrossRef

26.

Iosifidis A, Tefas A, Pitas I. Dynamic action recognition based on dynemes and extreme learning machine. Pattern Recognit Lett. 2013;34(15):1890–1898.

27.

Lu B, Wang G, Yuan Y, Han D. Semantic concept detection for video based on extreme learning machine. Neurocomputing. 2013;102:176–183.CrossRef

28.

Huang GB, Wang D, Lan Y. Extreme learning machines: a survey. Int J Mach Learn Cyber. 2011;2:107–122.CrossRef

29.

Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A. OP-ELM: Optimally pruned extreme learning machine. IEEE Transaction on Neural Networks, 2010;21(1):158–162.CrossRef

30.

Benoit F, Heeswijk M, Miche Y, Verleysen M, Lendasse A. Feature selection for nonlinear models with extreme learning machines. Neurocomputing. 2013;102:111–124.CrossRef

31.

Martinez-Martnez J, Escandell-Montero P, Soria-Olivas E, Martin-Guerrero J, Magdalena-Benedito R, Gomez-Sanchis J. Regularized extreme learning machine for regression problems. Neurocomputing. 2011;74:3716–3721.CrossRef

32.

Yu Q, Miche Y, Eirola E, Heeswijk M, Severin E, Lendasse A. Regularized extreme learning machine for regression with missing data. Neurocomputing. 2013;102:45–51.CrossRef

33.

Decherchi S, Gastaldo P, Leoncini A, Zunino R. Efficient digital implementation of extreme learning machines for classification. IEEE Transaction on Circuits and Systems, Part II, 2012;59(8).

34.

Li K, Zhang J, Xu H, Luo S, Li H. A Semi-supervised extreme learning machine method based on co-training. J Comput Inf Syst. 2013;9(1):207–214.

35.

Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the Alternating direction method of multipliers. Found Trends Mach Learn. 2010;3(1):1–122.

36.

Liu G, Lin Z, Yu Y. Robust subspace segmentation by low-rank representation, In: Proceedings of International Conference on Machine Learning (ICML), 2010; p. 663–670.

37.

Jun Y, Er MJ. An enhanced online sequential extreme learning machine algorithm, In: Proc. of Chinese Control and Decision Conference (CCDC), 2008;p. 2902–2907.

38.

Ross D, Lim J, Lin RS, Yang MH. Incremental learning for robust visual tracking. Int J Comput V. 2008;77(1):125–141.

39.

Mei X, Ling H. Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell. 2011;33(11):2259–2272.PubMedCrossRef

40.

Zhong W, Lu H, Yang MH. Robust object tracking via sparsity-based collaborative model, In: Proceedings of computer vision and pattern recognition (CVPR), 2012; p.1828–1845.

41.

Adam A, Rivlin E, Shimshoni I. Robust fragments-based tracking using the integral histogram. In: Proceedings of computer vision and pattern recognition (CVPR), 2006;p.798–805.

Titel: Multitask Extreme Learning Machine for Visual Tracking
verfasst von: Huaping Liu
Fuchun Sun
Yuanlong Yu
Publikationsdatum: 01.09.2014
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 3/2014
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-013-9242-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2014

Abductive Learning Ensembles for Hand Shape Identification

Development of Computational Models of Emotions for Autonomous Agents: A Review

An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels

Novel Biologically Inspired Approaches to Extracting Online Information from Temporal Data

A Learner-Independent Knowledge Transfer Approach to Multi-task Learning

Image Fusion by Hierarchical Joint Sparse Representation