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The use of bag of visual words (BOW) model for modelling images based onlocal invariant features computed at interest point locations has become astandard choice for many computer vision tasks. Visual vocabularies generatedfrom image feature vectors are expected to produce visual words that arediscriminative to improve the performance of image annotation systems. Mosttechniques that adopt the BOW model in annotating images declined favorableinformation that can be mined from image categories to build discriminativevisual vocabularies. To this end, this paper introduces a detailed frameworkfor automatically annotating natural scene images with local semantic labelsfrom a predefined vocabulary. The framework is based on a hypothesis thatassumes that, in natural scenes, intermediate semantic concepts are correlatedwith the local keypoints. Based on this hypothesis, image regions can beefficiently represented by BOW model and using a machine learning approach,such as SVM, to label image regions with semantic annotations. Anotherobjective of this paper is to address the implications of generating visualvocabularies from image halves, instead of producing them from the whole image,on the performance of annotating image regions with semantic labels. AllBOW-based approaches as well as baseline methods have been extensivelyevaluated on 6-categories dataset of natural scenes using the SVM and KNNclassifiers. The reported results have shown the plausibility of using the BOWmodel to represent the semantic information of image regions and thus toautomatically annotate image regions with labels.

Natural Scene Image Annotation Using Local Semantic Concepts and Spatial Bag of Visual Words