Adaptation and Learning in Automatic Systems
Adaptation and Learning in Automatic Systems
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
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A survey of methods for image annotation
Journal of Visual Languages and Computing
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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This work is devoted to the problem of automatic image annotation. This problem consists in assigning words of a natural language to an arbitrary image by analyzing textural characteristics (low-level features) of images without any other addtional information. It can help to extract intellectual information from images and to organize searching procedures in a huge image base according to a textual query. We propose the general annotation scheme based on the statistical classes and their classification. This scheme consists in the following. First we derive the low-level features of images that can be presented by histograms. After that we represent these histograms by statistical classes and compute secondary features based on introduced inclusion measures of statistical classes. The automatic annotation is produced by aggregating secondary features using linear decision functions.