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
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2016

01.01.2016 | Extreme Learning Machine and Applications

Principal pixel analysis and SVM for automatic image segmentation

verfasst von: Xuefei Bai, Wenjian Wang

Erschienen in: Neural Computing and Applications | Ausgabe 1/2016

Einloggen

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

search-config
loading …

Abstract

Segmenting objects from images is an important but highly challenging problem in computer vision and image processing. This paper presents an automatic object segmentation approach based on principal pixel analysis (PPA) and support vector machine (SVM), namely PPA–SVM. The method comprises three main steps: salient region extraction, principal pixel analysis, as well as SVM training and segmentation. We consider global saliency information and color feature by means of visual saliency detection and histogram analysis, such that SVM training data can be selected automatically. Experiment results on a public benchmark dataset demonstrate that, compared with some classical segmentation algorithms, the proposed PPA–SVM method can effectively segment the whole salient object with reasonable better performance and faster speed.
Vorheriger Artikel An efficient query processing optimization based on ELM in the cloud
Nächster Artikel Applying a new localized generalization error model to design neural networks trained with extreme learning machine

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

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

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+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
Literatur
1.
Hiremath PS, Jagadeesh P (2008) Content based image retrieval using color boosted salient points and shape features of an image. Int J Image Process 2(1):1–34CrossRef
2.
Guo CL, Zhang LM (2010) A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans Image Process 19(1):185–198CrossRefMathSciNet
3.
Yu Y, Mann GKI, Gosine RG (2010) An object-based visual attention model for robotic applications. IEEE Trans Syst Man Cybern B Cybern 40(5):1398–1412CrossRef
4.
Li H, Ngan KN (2008) Saliency model based face segmentation in head-and-shoulder video sequences. J Vis Commun Image Represent 19(5):320–333CrossRef
5.
Tsotsos JK, Culhane SM, Wai WYK, Lai Y, Davis N, Nuflo F (1995) Modelling visual attention via selective tuning. Artif Intell 78(1–2):507–545CrossRef
6.
Cheng MM, Zhang GX, Mitra NJ, Huang X, Hu SM (2011) Global contrast based salient region detection, CVPR 21–23
7.
Itti L, Koch C, Niebur E (1998) A model of saliency based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259CrossRef
8.
Harel J, Koch C, Perona P (2007) Graph-based visual saliency. Adv Neural Inf Process Syst 19:545–552
9.
Lin Y, Fang B, Tang Y (2010) A computational model for saliency maps by using local entropy. Proc Conf AAAI Artif Intell 967–973
10.
Walter D, Koch C (2006) Modelling attention to salient proto-object. Neural Netw 19(9):1395–1407CrossRef
11.
Valenti R, Sebe N, Gevers T (2009) Images saliency by isocentric curvedness and color, ICCV 2185–2192
12.
Ma YF, Zhang HJ (2003) Contrast-based image attention analysis by using fuzzy growing. International conference on multimedia, pp 374–381
13.
Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum HY (2011) Learning to de tect a salient object. IEEE Trans Pattern Anal Mach Intell 33(2):353–367CrossRef
14.
Hou X, Zhang L (2007) Saliency detection: a spectral residual approach, CVPR 1–8
15.
Guo C, Ma Q, Zhang L (2008) Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform, CVPR 1–8
16.
Achanta R, Hemami S, Esgtrada F, Süsstrunk S (2009) Frequency-tuned salient region detection, CVPR 1597–1604
17.
Rosin PL (2009) A simple method for detecting salient regions. Pattern Recognit 42(11):2363–2371MATHCrossRef
18.
Luo W, Li H, Liu G, Ngan KN (2011) Global salient information maximization for saliency detection. Sig Process Image Commun 27(3):238–248CrossRef
19.
Yang W, Tang Y, Fang B, Shang Z, Lin Y (2013) Visual saliency detection with center shift. Neurocomputing 103(1):63–74CrossRef
20.
Ouerhani N, Archip N, Hügli H, Erard PJ (2001) Visual attention guided seed selection for color image segmentation. Proceedings of the 9th international conference on computer analysis of image and patterns, lecture notes in computer science, vol 2124, Springer, London, pp 630–637
21.
Han J, Ngan KN, Li M, Zhang HJ (2006) Unsupervised extraction of visual attention objects in color images. IEEE Trans Circuits Syst 16(1):141–145
22.
Ko BC, Nam JY (2006) Object-of-interest segmentation based on human attention and semantic region clustering. J Opt Soc Am A 23(10):2462–2470CrossRef
23.
Achanta R, Estrada F, Wils P, Süsstrunk S (2008) Salient region detection and segmentation. In: Proceedings of the 6th international conference on computer vision systems, lecture notes in computer science, vol. 5008, Springer, Berlin pp 66–75
24.
Donoser M, Urschler M, Hirzer M, Bischof H (2009) Saliency driven total variation segmentation, ICCV 817–824
25.
Liu Z, Li W, Shen L, Han Z, Zhang Z (2010) Automatic segmentation of focused objects from images with low depth of field. Pattern Recognit Lett 31(7):572–581CrossRef
26.
Lee CY, Leou JJ, Hsiao HH (2012) Saliency-directed color image segmentation using modified particle swarm optimization. Signal Process 92(1):1–18CrossRef
27.
Liu Z, Shen L, Zhang Z (2011) Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction. Signal Process 91(2):290–299MATHCrossRef
28.
Zhu R, Yao M, Liu YM (2011) A two-level strategy for segmenting center of interest from pictures. Expert Syst Appl 38(3):1748–1759CrossRef
29.
Fu K, Gong C, Yang J, Zhou Y, Gu IYH (2013) Superpixel based color contrast and color distribution driven salient object detection. Signal Process Image Commun 28(10):1448–1463CrossRef
30.
31.
Yu Z, Wong HS, Wen G (2011) A modified support vector machine and its application to image segmentation. Image Vis Comput 29(1):29–40CrossRef
32.
Wang XY, Wang T, Bu J (2011) Color iamge segmentation using pixel wise support vector machine classification. Pattern Recognit 44(4):777–787MATHCrossRef
33.
Saha I, Maulik U, Bandyopadhyay S, Plewczynski D (2012) SVMeFC: SVM ensemble fuzzy clustering for satellite image segmentation. IEEE Trans Geosci Remote Sens 9(1):52–55CrossRef
34.
Zhao Q, Hu Y, Cao J (2009) Automatic image segmentation based on saliency maps and Fuzzy SVM, CCWMC 121–124
35.
Castleman KR (1996) Digital image processing, second ed. Prentice Hall, New York
36.
Wang P, Wang J, Zeng G, Feng J, Zha H, Li S (2012) Salient object detection for searched web images iva global saliency. In CVPR, 3194–3201
37.
Brigger P, Casas JR, Pardas M (1996) Morphological operators for image and video compression. IEEE Trans Image Process 5(6):881–898CrossRef
38.
Chen TW, Chen YL, Chien SY (2008) Fast image segmentation based on K-means clustering with histograms in HSV color space. IEEE workshop multimed. Signal Proc pp 322–325
39.
Zhang L, Lin FZ, Zhang B A CBIR method based on color-spatial feature, Technical report, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
40.
MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley symposium on mathematical statistics and probability 2:281–297
41.
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRef
42.
Rother C, Kolmogorov V, Blake A (2004) “Grabcut”–interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314CrossRef
43.
44.
Unnikrishnan R, Pantofaru C, Hebert M (2007) Toward objective evaluation of image segmentation algorithms. IEEE Trans Pattern Anal Mach Intell 29(6):929–943CrossRef
45.
Meila M (2005) Comparing clusterings: an axiomatic view. ICML 577–584
46.
Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, ICCV 416–425
47.
Wang XZ, Dong CR (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567CrossRef
48.
Wang XZ, Zhai JH, Lu SX (2008) Induction of multiple fuzzy decision trees based on rough set technique. Inf Sci 178(16):3188–3202MATHCrossRefMathSciNet
49.
Zhai JH, Xu HY, Wang XZ (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16(9):1493–1502CrossRef
Metadaten
Titel
Principal pixel analysis and SVM for automatic image segmentation
verfasst von
Xuefei Bai
Wenjian Wang
Publikationsdatum
01.01.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1544-2

Weitere Artikel der Ausgabe 1/2016

Neural Computing and Applications 1/2016 Zur Ausgabe

Retraction Note

Retraction Note to: Mass classification method in mammograms using correlated association rule mining

Extreme Learning Machine and Applications

An efficient query processing optimization based on ELM in the cloud

Extreme Learning Machine and Applications

Orthogonal incremental extreme learning machine for regression and multiclass classification

Extreme Learning Machine and Applications

A recurrent neural network for modeling crack growth of aluminium alloy

Extreme Learning Machine and Applications

Model predictive engine air-ratio control using online sequential extreme learning machine

Extreme Learning Machine and Applications

MR-ELM: a MapReduce-based framework for large-scale ELM training in big data era

Premium Partner

    Bildnachweise