Improving Scene Recognition through Visual Attention

  • Fernando López-García
  • Anton García-Díaz
  • Xose Ramon Fdez-Vidal
  • Xose Manuel Pardo
  • Raquel Dosil
  • David Luna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5524)


In this paper we study how the use of a novel model of bottom-up saliency (visual attention), based on local energy and color, can significantly accelerate scene recognition and, at the same time, preserve the recognition performance. To do so, we use a mobile robot-like application where scene recognition is performed through the use of SIFT features to characterize the different scenarios, and the Nearest Neighbor rule to carry out the classification. Experimental work shows that important reductions in the size of the database of prototypes can be achieved (17.6% of the original size) without significant losses in recognition performance (from 98.5% to 96.1%), thus accelerating the classification task.


Visual Attention Recognition Performance Database Size Sift Feature Scene Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fernando López-García
    • 1
  • Anton García-Díaz
    • 2
  • Xose Ramon Fdez-Vidal
    • 2
  • Xose Manuel Pardo
    • 2
  • Raquel Dosil
    • 2
  • David Luna
    • 2
  1. 1.Grupo de Visión por Computador, Departamento de Informática de Sistemas y ComputadoresUniversidad Politécnica de ValenciaValenciaSpain
  2. 2.Grupo de Visión Artificial, Departamento de Electrónica e ComputaciónUniversidade de Santiago de CompostelaSantiago de CompostelaSpain

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