Virtual Textual Representation for Efficient Image Retrieval
Published in: 3rd International Conference on Visual Information Engineering, VIE 2006
The state of the art in contemporary visual object categorization and classification is dominated by “Bag Of Words” approaches. These use either discriminative or generative learning models to learn the object or scene model. In this paper, we propose a novel “Bag of words” approach for content based image retrieval. Images are converted to virtual text documents and a new relevance feedback algorithm is applied on these documents. We explain how our approach is fundamentally different to existing ones and why it is ideally suited for CBIR. We also propose a new hybrid relevance feedback learning model. This merges the best of generative and discriminative approaches to achieve a robust and discriminative visual words based description of a visual concept. Our learning model and “Bag Of Words” approach achieve a balance between good classification and efficient image retrieval.