Affordable Access

Publisher Website

Exploiting global rarity, local contrast and central bias for salient region learning

Authors
Journal
Neurocomputing
0925-2312
Publisher
Elsevier
Identifiers
DOI: 10.1016/j.neucom.2014.04.003
Keywords
  • Global Rarity
  • Local Contrast
  • Central Bias
  • Salient Region
  • Learning
Disciplines
  • Computer Science

Abstract

Abstract In this paper, we are to present a model that integrates and benefits from the global rarity, local contrast and central bias for saliency detection. Previous saliency works only consider one or two of them. Further, to avoid some inherent drawbacks of existing three factors, we first over-segment the image into many small coherent regions. And then, we exploit the self-information and regional de-noising, regional contrast and consistency, Gaussian function and regional averaging to get three new factors of global rarity, local contrast and central bias. Finally, we embed them into a nonlinear neural network to figure out their own contributions in saliency detection. Extensive experiments and comparisons illustrate the effectiveness of our saliency model with three new built factors.

There are no comments yet on this publication. Be the first to share your thoughts.