PicSOM: self-organizing maps for content-based image retrieval

Abstract

Content-based image retrieval is an important approach to the problem of processing the increasing amount of visual data. It is based on automatically extracted features from the content of the images, such as color, texture, shape and structure. We have started a project to study methods for content-based image retrieval using the self-organizing map (SOM) as the image similarity scoring method. Our image retrieval system, named PicSOM, can be seen as a SOM-based approach to relevance feedback which is a form of supervised learning to adjust the subsequent queries based on the user's responses during the information retrieval session. In PicSOM, a separate tree structured SOM (TS-SOM) is trained for each feature vector type in use. The system then adapts to the user's preferences by returning her more images from those SOMs where her responses have been most densely mapped.