Background: Most techniques that adopt the BOW model in annotating images declined favorable
information that can be mined from image categories to build discriminative visual vocabularies. We introduce
a detailed framework for automatically annotating natural scene images with local semantic labels
from a predefined vocabulary.
Methods: The proposed framework is based on a hypothesis that assumes that, in natural scenes, intermediate
semantic concepts are correlated with the local keypoints. Based on this hypothesis, image regions can
be efficiently represented by BOW model and using a machine learning approach, such as SVM, to label
image regions with semantic annotations. Another objective of this paper is to address the implications of
generating visual vocabularies from image halves, instead of producing them from the whole image, on the
performance of annotating image regions with semantic labels.
Results: The reported results have shown the plausibility of using the BOW model to represent the semantic
information of image regions and thus to automatically annotate image regions with semantic labels.
Conclusion: Our experimental results shows the plausibility of local from global approach for image region
annotation as well as the discriminative power of using visual vocabularies from image halves. It showed an
improved annotation results using integrated bag of visual words combined with low-level features.