Concept learning by fuzzy k-NN classification and relevance feedback for efficient image retrieval
A new method for combining visual and semantic features in image retrieval is presented. A fuzzy k-NN classifier assigns initial semantic labels to database images. These labels are gradually modified by relevance feedbacks from the users. Experimental results on a database of 1000 images from 10 semantic groups are reported.
relevance feedback semantic feature semantic network fuzzy classification content-based image retrieval