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access icon free Global and local exploitation for saliency using bag-of-words

  • Author(s): Zhenzhu Zheng 1 ; Yun Zhang 1 ; Luxin Yan 2
    • View affiliations
    • Affiliations: 1: High Performance Computing Center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China;
      2: Science and Technology on Multi-spectral Information Processing Laboratory, School of Automation, Huazhong University of Science & Technology, Wuhan, People's Republic of China
  • Source: Volume 8, Issue 4, August 2014, p. 299 – 304
    DOI:  10.1049/iet-cvi.2013.0132 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Received 02/06/2013, Accepted 14/11/2013, Revised 28/10/2013, Published 14/01/2014

The guidance of attention helps human vision system to detect objects rapidly. In this study, the authors present a new saliency detection algorithm by using bag-of-words (BOW) representation. The authors regard salient regions as coming from globally rare features and regions locally differ from their surroundings. Our approach consists of three stages: first, calculate global rarity of visual words. A vocabulary, a group of visual words, is generated from the given image and a rarity factor for each visual word is introduced according to its occurrence. Second, calculate local contrast. Representations of local patch are achieved from the histograms of words. Then, local contrast is computed by the difference between the two BOW histograms of a patch and its surroundings. Finally, saliency is measured by the combination of global rarity and local patch contrast. We compare our model with the previous methods on natural images, and experimental results demonstrate good performance of our model and fair consistency with human eye fixations.

Inspec keywords: object detection; image representation

Other keywords: saliency detection algorithm; objects detection; BOW representation; human vision system; salient regions; local patch representation; bag-of-words representation; natural images

Subjects: Optical, image and video signal processing; Computer vision and image processing techniques

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