Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2013 | OriginalPaper | Buchkapitel

Learning a Family of Detectors via Multiplicative Kernels

verfasst von : Quan Yuan, Ashwin Thangali, Vitaly Ablavsky, Stan Sclaroff

Erschienen in: Topics in Medical Image Processing and Computational Vision

Verlag: Springer Netherlands

Einloggen

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

search-config
loading …

Abstract

Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. Model training is accomplished via standard SVM learning. When the foreground object masks are provided in training, the detectors can also produce object segmentations. A tracking-by-detection framework to recover foreground state in video sequences is also proposed with our model. The advantages of our method are demonstrated on tasks of object detection, view angle estimation and tracking. Our approach compares favorably to existing methods on hand and vehicle detection tasks. Quantitative tracking results are given on sequences of moving vehicles and human faces.

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
Nächstes Kapitel Facial Expression Recognition Using FAPs-Based 3DMMM
Fußnoten
1
In this paper, all vector variables are column vectors.
 
Literatur
1.
Agarwal A, Triggs B (2004) 3D human pose from silhouettes by relevance vector regression. In: Proceedings of the IEEE conference on computer vision and pattern recognition
2.
Andriluka M, Roth S, Schiele B (2008) People-tracking-by-detection and people-detection-by-tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition
3.
Athitsos V, Sclaroff S (2003) Estimating 3D hand pose from a cluttered image. In: Proceedings of the IEEE conference on computer vision and pattern recognition
4.
Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(24):509–522CrossRef
5.
Bissacco A, Yang M, Soatto S (2006) Deteing humans via their pose. In: Proceedings of advances in neural information processing systems
6.
Blaschko MB, Lampert CH (2008) Learning to localize objects with structured output regression. In: Proceedings of the European conference on computer vision
7.
Borenstein E, Ullman S (2002) Class-specific, top-down segmentation. In: Proceedings of the European conference on computer vision
8.
Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297MATH
9.
Crasborn O, van der Kooij E, Nonhebel A, Emmerik W (2004) ECHO data set for sign language of the Netherlands. Technical report Department of Linguistics, University Nijmegen
10.
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition
11.
Damoulas T, Girolami MA (2008) Pattern recognition with a Bayesian kernel combination machine. Pattern Recogn Lett 30(1):46–54CrossRef
12.
Enzweiler M, Gavrila DM (2008) A mixed generative-discriminative framework for pedestrian classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition
13.
Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2009) Object detection with discriminatively trained part based models. IEEE Trans Pattern Anal Mach Intell (to appear)
14.
Felzenszwalb PF, Huttenlocher DP (2005) Pictorial structures for object recognition. Int J Comput Vision 61:55–79CrossRef
15.
Gavrila DM (2000) Pedestrian detection from a moving vehicle. In: Proceedings of the European conference on computer vision
16.
Gross R, Matthews I, Cohn J, Kanade T, Baker S (2008) Multi-PIE. In: Proceedings of the IEEE international conference on face and gesture recognition
17.
Hoiem D, Efros AA, Hebert M (2008) Putting objects in perspective. Int J Comput Vision 80(1):3–15CrossRef
18.
Huang C, Ai H, Li Y, Lao S (2007) High-performance rotation invariant multiview face detection. IEEE Trans Pattern Anal Mach Intell 29(4):671–686CrossRef
19.
Ioffe C, Forsyth D (2001) Probabilistic methods for finding people. Int J Comput Vision 43(1):45–68MATHCrossRef
20.
Ionescu C, Bo L, Sminchisescu C (2009) Structural SVM for visual localization and continuous state estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition
21.
Isard M, Blake A (1998) CONDENSATION: Conditional density propagation for visual tracking. Int J Comput Vision 29(1):5–28CrossRef
22.
Joachims T (1999) Making large-scale SVM learning practical. In: Scholkopf B, Burges C, Smola A (eds) Advances in Kernel methods—support vector learning. MIT Press, Cambridge
23.
Kumar MP, Torr PHS, Zisserman A (2005) Obj Cut. In: Proceedings of the IEEE conference on computer vision and pattern recognition
24.
Leibe B, Cornelis N, Cornelis K, Gool LV (2007) Dynamic 3D scene analysis from a moving vehicle. In: Proceedings of the IEEE conference on computer vision and pattern recognition
25.
Leibe B, Leonardis A, Schiele B (2007) Robust object detection with interleaved categorization and segmentation. Int J Comput Vision 77(1):259–289CrossRef
26.
Li S, Fu Q, Gu L, Scholkopf B, Cheng Y, Zhang H (2001) Kernel machine based learning for multi-view face detection and pose estimation. In: Proceedings of the IEEE international conference on computer vision
27.
Li S, Zhang Z (2004) Floatboost learning and statistical face detection. IEEE Trans Pattern Anal Mach Intell 26(9):1112–1123CrossRef
28.
Li Y, Ai H, Yamashita T, Lao S, Kawade M (2008) Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Trans Pattern Anal Mach Intell 30(10):1728–1740CrossRef
29.
Everingham M et al (2006) The 2005 PASCAL visual object class challenge. In: Machine learning challenges—evaluating predictive uncertainty, visual object classification, and recognising textual entailment, Springer
30.
Marszalek M, Schmid C, Harzallah H, van de Weijer J (2007) Learning object representations for visual object class recognition. In: Visual recognition challange workshop, in conjunction with ICCV
31.
Murase H, Nayar SK (1995) Visual learning and recognition of 3D objects from appearance. Int J Comput Vision 14(1):5–24CrossRef
32.
33.
Nocedal J, Wright SJ (2006) Numerical optimization. Springer, New York
34.
Oikonomopoulos A, Patras I, Pantic M (2006) Kernel-based recognition of human actions using spatiotemporal salient points. In: Workshop on vision for human computer interaction
35.
Okuma K, Taleghani A, Freitas ND, Little J, Lowe D (2004) A boosted particle filter: multitarget detection and tracking. In: Proceeedings of the European conference on computer vision
36.
Ong E, Bowden R (2004) A boosted classifier tree for hand shape detection. In: Proceedings of the IEEE international conference on face and gesture recognition
37.
Osadchy R, Miller M, LeCun Y (2004) Synergistic face detection and pose estimation with energy-based model. In: Proceedings of advances in neural information processing systems
38.
Papageorgiou C, Poggio T (2000) A trainable system for object detection. Int J Comput Vision 38(1):15–33MATHCrossRef
39.
Pentland A, Moghaddam B, Starner T (1994) View-based and modular eigenspaces for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition
40.
Platt J (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola A, Bartlett P, Scholkopf B, Schuurmans D (eds) Advances in large margin classifiers. MIT Press, Cambridge
41.
Ramanan D, Forsyth DA, Zisserman A (2005) Strike a pose: tracking people by finding stylized poses. In: Proceedings of the IEEE conference on computer vision and pattern recognition
42.
Rifkin R, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101–141
43.
Rosales R, Sclaroff S (2002) Learning body pose via specialized maps. In: Proceedings of advances in neural information processing systems
44.
Russell BC, Torralba A, Murphy KP, Freeman WT (2005) LabelMe: a database and web-based tool for image annotation. Technical report, MIT Press, Cambridge
45.
Seemann E, leibe B, Schiele B (2006) Multi-aspect detection of articulated objects. In: Proceedings of the IEEE conference on computer vision and pattern recognition
46.
Shakhnarovich G, Viola P, Darrell T (2003) Fast pose estimation with parameter-sensitive hashing. In: Proceedings of the IEEE international conference on computer vision
47.
Shi J, Malik J (1997) Normalized cuts and image segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition
48.
Sidenbladh H, Black MJ, Fleet DJ (2000) Stochastic tracking of 3D human figures using 2D image motion. In: Proceedings of the European conference on computer vision, pp 702–718
49.
Sigal L, Bhatia S, Roth S, Black M, Isard M (2004) Tracking loose-limbed people. In: Proceedings of the IEEE conference on computer vision and pattern recognition
50.
Sminchisescu C, Kanaujia A, Metaxas D (2006) Learning joint top-down and bottom-up processes for 3D visual inference. In: Proceedings of the IEEE conference on computer vision and pattern recognition
51.
Stenger B, Thayananthan A, Torr P, Cipolla R (2003) Filtering using a tree-based estimator. In: Proceedings of the IEEE international conference on computer vision
52.
Torralba A, Murphy K, Freeman W (2004) Sharing features: Efficient boosting procedures for multiclass object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition
53.
Varma M, Ray D (2007) Learning the discriminative power-invariance trade-off. In: Proceedings of the IEEE international conference on computer vision. Rio de Janeiro, Brazil
54.
Viola P, Jones M (2003) Fast multi-view face detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition
55.
Viola P, Jones M (2004) Robust real time object detection. Int J Comput Vision 57(2):137–154CrossRef
56.
Wang L, Shi J, Song G, Shen I (2007) Object detection combining recognition and segmentation. In: Proceedings of Asian conference on computer vision
57.
Wu B, Nevatia R (2007) Cluster boosted tree classifier for multi-view multi-pose object detection. In: Proceedings of the IEEE international conference on computer vision
58.
Wu B, Nevatia R (2007) Simultaneous object detection and segmentation by boosting local shape feature based classifier. In: Proceedings of the IEEE conference on computer vision and pattern recognition
59.
Yuan Q, Thangali A, Ablavsky V, Sclaroff S (2007) Parameter sensitive detectors. In: Proceedings of the IEEE conference on computer vision and pattern recognition
60.
Zhu L, Chen Y, Lin C, Yuille AL (2007) Rapid inference on a novel and/or graph: detection, segmentation and parsing of articulated deformable objects in cluttered backgrounds. In: Proceedings of advances in neural information processing systems
Metadaten
Titel
Learning a Family of Detectors via Multiplicative Kernels
verfasst von
Quan Yuan
Ashwin Thangali
Vitaly Ablavsky
Stan Sclaroff
Copyright-Jahr
2013
Verlag
Springer Netherlands
Buch
Topics in Medical Image Processing and Computational Vision

Print ISBN: 978-94-007-0725-2

Electronic ISBN: 978-94-007-0726-9

Copyright-Jahr: 2013

DOI
https://doi.org/10.1007/978-94-007-0726-9_1

Neuer Inhalt

    Bildnachweise