Contrast-based image attention analysis by using fuzzy growing

ABSTRACT

Visual attention analysis provides an alternative methodology to semantic image understanding in many applications such as adaptive content delivery and region-based image retrieval. In this paper, we propose a feasible and fast approach to attention area detection in images based on contrast analysis. The main contributions are threefold: 1) a new saliency map generation method based on local contrast analysis is proposed; 2) by simulating human perception, a fuzzy growing method is used to extract attended areas or objects from the saliency map; and 3) a practicable framework for image attention analysis is presented, which provides three-level attention analysis, i.e., attended view, attended areas and attended points. This framework facilitates visual analysis tools or vision systems to automatically extract attentions from images in a manner like human perception. User study results indicate that the proposed approach is effective and practicable.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
        I.4.10 Image Representation
            Subjects: Statistical

Additional Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
        I.4.10 Image Representation
            Subjects: Hierarchical
      I.4.6 Segmentation
          Subjects: Region growing, partitioning; Pixel classification

General Terms:
Algorithms, Design, Experimentation, Theory

Keywords:
attention detection, contrast analysis, fuzzy growing, image analysis, visual attention model