Efficient Computation of Attributes and Saliency Maps on Tree-Based Image Representations

From LRDE

Abstract

Tree-based image representations are popular tools for many applications in mathematical morphology and image processing. Classically, one computes an attribute on each node of a tree and decides whether to preserve or remove some nodes upon the attribute function. This attribute function plays a key role for the good performance of tree-based applications. In this paper, we propose several algorithms to compute efficiently some attribute information. The first one is incremental computation of information on region, contour, and context. Then we show how to compute efficiently extremal information along the contour (e.g., minimal gradient's magnitude along the contour). Lastly, we depict computation of extinction-based saliency map using tree-based image representations. The computation complexity and the memory cost of these algorithms are analyzed. To the best of our knowledgeexcept information on region, none of the other algorithms is presented explicitly in any state-of-the-art paper.

Documents

Bibtex (lrde.bib)

@InProceedings{	  xu.15.ismm,
  author	= {Yongchao Xu and Edwin Carlinet and Thierry G\'eraud and
		  Laurent Najman},
  title		= {Efficient Computation of Attributes and Saliency Maps on
		  Tree-Based Image Representations},
  booktitle	= {Mathematical Morphology and Its Application to Signal and
		  Image Processing -- Proceedings of the 12th International
		  Symposium on Mathematical Morphology (ISMM)},
  year		= {2015},
  series	= {Lecture Notes in Computer Science Series},
  volume	= {9082},
  address	= {Reykjavik, Iceland},
  publisher	= {Springer},
  editor	= {J.A. Benediktsson and J. Chanussot and L. Najman and H.
		  Talbot},
  pages		= {693--704},
  abstract	= {Tree-based image representations are popular tools for
		  many applications in mathematical morphology and image
		  processing. Classically, one computes an attribute on each
		  node of a tree and decides whether to preserve or remove
		  some nodes upon the attribute function. This attribute
		  function plays a key role for the good performance of
		  tree-based applications. In this paper, we propose several
		  algorithms to compute efficiently some attribute
		  information. The first one is incremental computation of
		  information on region, contour, and context. Then we show
		  how to compute efficiently extremal information along the
		  contour (e.g., minimal gradient's magnitude along the
		  contour). Lastly, we depict computation of extinction-based
		  saliency map using tree-based image representations. The
		  computation complexity and the memory cost of these
		  algorithms are analyzed. To the best of our knowledge,
		  except information on region, none of the other algorithms
		  is presented explicitly in any state-of-the-art paper.}
}