Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A database centric view of semantic image annotation and retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Multiple Class Machine Learning Approach for an Image Auto-Annotation Problem
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Image Annotation Refinement Using Web-Based Keyword Correlation
SAMT '09 Proceedings of the 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Image similarities on the basis of visual content: an attempt to bridge the semantic gap
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Detection of similar sequences in EEG maps series using correlation coefficients matrix
Machine Graphics & Vision International Journal
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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 autoannotation 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.