Skip to main content
Erschienen in: Cluster Computing 4/2019

13.11.2017

RETRACTED ARTICLE: Multimedia and multi-feature cluster fusion model based on saliency for mobile network applications

verfasst von: Zhenze Jia, Xiaoguang Fan, Haoxiang Wang

Erschienen in: Cluster Computing | Sonderheft 4/2019

Einloggen

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

search-config
loading …

Abstract

This paper introduces the concept, advantages, structure, method and application of multisensor integration and data fusion, and lists four different integration characteristics of sensors. Data fusion technology combines data from different sensors or other information sources in order to improve the accuracy of location and feature estimation. In the process of data fusion, modeling includes signal model, noise model, converter model, data transformation model and fusion model. The data fusion model includes the fusion method and the structure. This paper introduces the integrated, distributed and hybrid fusion structures, and compares them. The visual saliency map to image processing technology depends on the quality of the obtained good results, the existing visual saliency detection method is usually only detected by visual saliency map attribute rough, seriously affected the image processing results. Therefore, a visual saliency detection method based on Bayesian theory and statistical learning is proposed to detect the visual saliency of the image. The method is based on Bayesian theory of the significance of static images. According to the bottom-up visual saliency model, the ROC curve was used for quantitative evaluation in the two standard data sets. The results show that the nonlinear combination effect is better than the linear combination.

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!

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!

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!

Literatur
1.
Zurück zum Zitat Yang, Y., et al.: Multi-feature fusion via hierarchical regression for multimedia analysis. IEEE Trans. Multimed. 15(3), 572–581 (2013)CrossRef Yang, Y., et al.: Multi-feature fusion via hierarchical regression for multimedia analysis. IEEE Trans. Multimed. 15(3), 572–581 (2013)CrossRef
2.
Zurück zum Zitat Wang, H., Chaoqun, W., Junsong, Y.: Visual Clustering with Minimax Feature Fusion. Visual Pattern Discovery and Recognition, pp. 67–83. Springer, Singapore (2017) Wang, H., Chaoqun, W., Junsong, Y.: Visual Clustering with Minimax Feature Fusion. Visual Pattern Discovery and Recognition, pp. 67–83. Springer, Singapore (2017)
3.
Zurück zum Zitat Shi, Lu-kui, Zhou, Hao, Liu, Wen-hao: Multi-feature fusion and visualization of pavement distress images based on manifold learning. J. Highw. Transp. Res. Dev. (Engl. Edition) 11(1), 14–22 (2017)CrossRef Shi, Lu-kui, Zhou, Hao, Liu, Wen-hao: Multi-feature fusion and visualization of pavement distress images based on manifold learning. J. Highw. Transp. Res. Dev. (Engl. Edition) 11(1), 14–22 (2017)CrossRef
4.
Zurück zum Zitat Chao, W., et al.: A multi-layer power transformer life span evaluating decision model based on information fusion. In: 2014 International Conference on High Voltage Engineering and Application (ICHVE). IEEE (2014) Chao, W., et al.: A multi-layer power transformer life span evaluating decision model based on information fusion. In: 2014 International Conference on High Voltage Engineering and Application (ICHVE). IEEE (2014)
5.
Zurück zum Zitat Wang, H., Wang, J.: An effective image representation method using kernel classification. In: Proceedings 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 853–858. IEEE (2014) Wang, H., Wang, J.: An effective image representation method using kernel classification. In: Proceedings 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 853–858. IEEE (2014)
7.
Zurück zum Zitat Li, Y., Sun, L., Sun, X.: Automatic tracking of pig feeding behavior based on particle filter with multi-feature fusion. Trans. Chin. Soc. Agric. Eng. 33(1), 246–252 (2017) Li, Y., Sun, L., Sun, X.: Automatic tracking of pig feeding behavior based on particle filter with multi-feature fusion. Trans. Chin. Soc. Agric. Eng. 33(1), 246–252 (2017)
9.
Zurück zum Zitat Lv, X., et al.: Rgb-d hand-held object recognition based on heterogeneous feature fusion. J. Comput. Sci. Technol. 30(2), 340 (2015)CrossRef Lv, X., et al.: Rgb-d hand-held object recognition based on heterogeneous feature fusion. J. Comput. Sci. Technol. 30(2), 340 (2015)CrossRef
10.
Zurück zum Zitat Xiang, Z., Xueqiang L., Kai Z.: An image classification method based on multi-feature fusion and multi-kernel SVM. In: Proceedings 2014 Seventh International Symposium on Computational Intelligence and Design (ISCID), Vol. 2. IEEE (2014) Xiang, Z., Xueqiang L., Kai Z.: An image classification method based on multi-feature fusion and multi-kernel SVM. In: Proceedings 2014 Seventh International Symposium on Computational Intelligence and Design (ISCID), Vol. 2. IEEE (2014)
11.
Zurück zum Zitat Liu, C., Zhao, L., Tang, H.: Reduction of false alarms in intensive care unit using multi-feature fusion method. In: Proceedings Computing in Cardiology Conference (CinC), 2015. IEEE (2015) Liu, C., Zhao, L., Tang, H.: Reduction of false alarms in intensive care unit using multi-feature fusion method. In: Proceedings Computing in Cardiology Conference (CinC), 2015. IEEE (2015)
12.
Zurück zum Zitat Liang, R.Z., Shi, L., Wang, H., Meng, J., Wang, J.J.Y., Sun, Q., Gu, Y.: Optimizing top precision performance measure of content-based image retrieval by learning similarity function. In: Proceedings 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2954–2958. IEEE (2016) Liang, R.Z., Shi, L., Wang, H., Meng, J., Wang, J.J.Y., Sun, Q., Gu, Y.: Optimizing top precision performance measure of content-based image retrieval by learning similarity function. In: Proceedings 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2954–2958. IEEE (2016)
13.
Zurück zum Zitat Althloothi, S., et al.: Human activity recognition using multi-features and multiple kernel learning. Pattern Recognit. 47(5), 1800–1812 (2014)CrossRef Althloothi, S., et al.: Human activity recognition using multi-features and multiple kernel learning. Pattern Recognit. 47(5), 1800–1812 (2014)CrossRef
15.
Zurück zum Zitat Roy, S.D., et al.: Camera-based document image matching using multi-feature probabilistic information fusion. Pattern Recognit. Lett. 58, 42–50 (2015)CrossRef Roy, S.D., et al.: Camera-based document image matching using multi-feature probabilistic information fusion. Pattern Recognit. Lett. 58, 42–50 (2015)CrossRef
16.
Zurück zum Zitat Dev, S., Wen, B., Lee, Y.H., Winkler, S.: Ground-based image analysis: a tutorial on machine-learning techniques and applications. IEEE Geosci. Remote Sens. Mag. 4(2), 79–93 (2016)CrossRef Dev, S., Wen, B., Lee, Y.H., Winkler, S.: Ground-based image analysis: a tutorial on machine-learning techniques and applications. IEEE Geosci. Remote Sens. Mag. 4(2), 79–93 (2016)CrossRef
17.
Zurück zum Zitat Hellwing, W.A., Koyama, K., Bose, B., Zhao, G.B.: Revealing modified gravity signal in matter and halo hierarchical clustering. arXiv preprint arXiv:1703.03395 (2017) Hellwing, W.A., Koyama, K., Bose, B., Zhao, G.B.: Revealing modified gravity signal in matter and halo hierarchical clustering. arXiv preprint arXiv:​1703.​03395 (2017)
18.
Zurück zum Zitat Zareapoor, M., Shamsolmoali, P., Jain, D.K., Wang, H., Yang, J.: Kernelized support vector machine with deep learning: an efficient approach for extreme multiclass dataset. Pattern Recognit. Lett. (2017) Zareapoor, M., Shamsolmoali, P., Jain, D.K., Wang, H., Yang, J.: Kernelized support vector machine with deep learning: an efficient approach for extreme multiclass dataset. Pattern Recognit. Lett. (2017)
Metadaten
Titel
RETRACTED ARTICLE: Multimedia and multi-feature cluster fusion model based on saliency for mobile network applications
verfasst von
Zhenze Jia
Xiaoguang Fan
Haoxiang Wang
Publikationsdatum
13.11.2017
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 4/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1335-0

Weitere Artikel der Sonderheft 4/2019

Cluster Computing 4/2019 Zur Ausgabe