multi-level annotation of natural scenes using dominant image components and semantic concepts
Automatic image annotation is a promising solution to enable semantic image retrieval via keywords. In this paper, we propose a multi-level approach to annotate the semantics of   natural scenes   by using both the dominant image components (salient objects) and the relevant semantic concepts. To achieve automatic image annotation at the content level, we use salient objects as the dominant image components for image content representation and feature extraction. To support automatic image annotation at the concept level, a novel image classification technique is developed to map the images into the most relevant semantic image concepts. In addition, Support Vector Machine (SVM) classifiers are used to learn the detection functions for the pre-defined salient objects and finite mixture models are used for semantic concept interpretation and modeling. An   adaptive EM algorithm   has been proposed to determine the optimal model structure and model parameters simultaneously. We have also demonstrated that our algorithms are very effective to enable multi-level annotation of   natural scenes   in a large-scale image dataset.
annotation use imaging imaging component semantic concept automatic image annotation image annotation image retrieval paper salient objects object relevance contention representation feature extraction support image classification support vector machine classifiation learning detection mixture model model interpretation adaptive em algorithm algorithm optimality structure effect large-scale image