Open AccessGlobal and local exploitation for saliency using bag‐of‐words
Author(s) -
Zheng Zhenzhu,
Zhang Yun,
Yan Luxin
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0132
Subject(s) - contrast (vision) , histogram , artificial intelligence , salient , computer science , bag of words model , pattern recognition (psychology) , vocabulary , computer vision , consistency (knowledge bases) , representation (politics) , word (group theory) , bag of words model in computer vision , image (mathematics) , visual word , mathematics , image retrieval , philosophy , linguistics , politics , political science , law , geometry
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.