Visual Saliency by Keypoints Distribution Analysis

  • Edoardo Ardizzone
  • Alessandro Bruno
  • Giuseppe Mazzola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

In this paper we introduce a new method for Visual Saliency detection. The goal of our method is to emphasize regions that show rare visual aspects in comparison with those showing frequent ones. We propose a bottom up approach that performs a new technique based on low level image features (texture) analysis. More precisely, we use SIFT Density Maps (SDM), to study the distribution of keypoints into the image with different scales of observation, and its relationship with real fixation points. The hypothesis is that the image regions that show a larger distance from the mode (most frequent value) of the keypoints distribution over all the image are the same that better capture our visual attention. Results have been compared to two other low-level approaches and a supervised method.

Keywords

saliency visual attention texture SIFT 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Edoardo Ardizzone
    • 1
  • Alessandro Bruno
    • 1
  • Giuseppe Mazzola
    • 1
  1. 1.Dipartimento di Ingegneria Chimica, Gestionale, Informatica e Meccanica.Università degli Studi di PalermoPalermoItaly

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