Automatic Image Annotation is important research topic in machine vision as it enables one to retrieve images from large databases by using textual queries. In recent years many machine learning techniques have been proposed to build detectors of concepts present on the images. In this paper we present a novel approach for image auto-annotation based on transfer of annotations from most similar images to the query image. We model image features by Multivariate Gaussian Distribution and measure distance between images by using Jensen-Shannon divergence. In spite of its simplicity, the proposed solution outperforms the state-of-the-art methods for image annotation and thus can be used as a baseline for developing other more elaborate methods.
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