Using relevance feedback to learn visual concepts from image instances

Abstract

This paper presents a novel method to retrieve images by learning the embedded visual concept from a set of given examples. Through a user's relevance feedback, the visual concept can be effectively learned to classify images which contain common visual entities. The learning process is started by providing a set of either positive or negative training examples and is then interactively adjusted according to the user's relevance feedback. In contrast to traditional methods, the proposed method utilizes a novel way to overcome the under-training problem which is frequently suffered in the learning process. Since no time-consuming optimization process is involved, the proposed method learns the visual concepts extremely fast. Therefore, the target concept can be learned on-line and is user-adaptable for effective retrieval of image contents. Experimental results are provided to prove the superiority of the proposed method.