Top-Down Biasing and Modulation for Object-Based Visual Attention

  • Alcides Xavier Benicasa
  • Marcos G. Quiles
  • Liang Zhao
  • Roseli A. F. Romero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)


This work presents a new object-based visual attention model with bottom-up and top-down features. Bottom-up attention is related to the contrast of primitive visual features, such as color, orientation, and intensity. On the other hand, top-down attention is related to the intentions of the viewer and can be seen as a modulation process through the selection system. Thus, if the viewer is searching for an specific shape or color, the top-down modulation can bias the searching process in relation to those features. Our model is composed of five main modules which are responsible for the extraction of the visual features, image segmentation, object recognition, object-saliency map, and object selection. Results on natural images are compared with state-of-the-art approaches and an ground truth fixation maps for a variety of images revealing the efficacy of the proposed approach for visual attention.


top-down biasing bottom-up and top-down visual attention object-based attention recognition of objects 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alcides Xavier Benicasa
    • 1
  • Marcos G. Quiles
    • 2
  • Liang Zhao
    • 3
  • Roseli A. F. Romero
    • 3
  1. 1.Federal University of SergipeItabaianaBrazil
  2. 2.Federal University of São PauloSão PauloBrazil
  3. 3.University of São PauloSão CarlosBrazil

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