Image clustering using self-organizing feature map with refinement

Introduces a new approach for image retrieval systems using a self organizing feature map (SOM) as one of the feature extracting algorithms. In the authors´ approach, they take a SOM network where images are indexed and organized automatically so that the users can retrieve images by visually browsing the organized image space. For image indexing, the objects in an image are first analyzed for their shape features such as roundness, rectangularity, ellipticity, eccentricity, bending energy. These features are used to form a feature vector that represents the image. Subsequently, the feature vectors representing all the images in an image database are organized by SOM. And then the authors propose a method for which the system can be adapted when information is changed or appended. Since this image feature map reflects the statistical patterns, i.e., the inter-similarities of the objects, the relationships among the images can be recognized by their location, neighborhood, and the way the map is organized