Amplifying the differences between your positive samples and neighbors in image retrieval

Abstract

A novel method for retrieving images based on relevance feedback and clustering has been developed. That is, by clustering sets of retrieved data, a user can select some good answers from them by considering the difference between the feature data of the selected images and the feature data of images placed in their neighborhood. This difference information improves previous queries since the user must have found some important difference between their-selected image and similar neighboring images. An image-retrieval system based on a relevance feedback by difference amplification is set up and shown to be more effective than conventional methods.