Semi-Supervised Image Meta-Filtering Using Relevance Feedback in Cultural Heritage Applications

摘要

An image filtering scheme for images of cultural interest is presented. The proposed methodology utilize a semi supervised approach for the creation of an appropriate distance learning metric, which is used for the filtering. User's feedback is involved only for a minor set of data, defined using OPTICS algorithm and sparse modeling representative selection. Such an approach facilitates the refinement of retrieval results, always under the scope of the user needs. The described methodology can be easily implemented for a variety of feature vectors and data sets.