A nonlinear feature fusion by variadic neural network in saliency-based visual attention

Zahra Kouchaki*, Ali Motie Nasrabadi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

Abstract

This study presents a novel combinational visual attention system which applies both bottom-up and topdown information. This can be employed in further processing such as object detection and recognition purpose. This biologically-plausible model uses nonlinear fusion of feature maps instead of simple superposition by employing a specific Artificial Neural Network (ANN) as combination operator. After extracting 42 feature maps by Itti's model, they are weighed purposefully through several training images with their corresponding target masks to highlight the target in the final saliency map. In fact, the weights of 42 feature maps are proportional to their influence on finding target in the final saliency map. The lack of bottom-up information is compensated by applying top-down information with available target masks. Our model could automatically detect the conceptual features of desired object only by considering the target information. We have tried to model the process of combining 42 feature maps to form saliency map by applying the neural network which resembles biological neural network. The Experimental results and comparing our model with the basic saliency model using 32 images of test dataset indicate a noticeable improvement in finding target in the first hit.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Computer Vision Theory and Applications, VISAPP 2012
EditorsGabriela Csurka, José Braz
Place of PublicationSetúbal, Portugal
PublisherSciTePress
Pages457-461
Number of pages5
Volume1
ISBN (Print)9789898565037
DOIs
Publication statusPublished - 2012
EventInternational Conference on Computer Vision Theory and Applications, VISAPP 2012 - Rome, Italy
Duration: 24 Feb 201226 Feb 2012

Other

OtherInternational Conference on Computer Vision Theory and Applications, VISAPP 2012
CountryItaly
CityRome
Period24/02/1226/02/12

Keywords

  • Neural network
  • Nonlinear fusion
  • Object detection
  • Saliency map
  • Visual attention

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