Automatic Image Annotation Using GHSOM

Due to the improvement of digital image technology and increasing amount of digital image data, the issue of automatic image annotation technology and applications becomes more and more important. In order to retrieval image efficiently, it is important to extract and represent the semantics of images. Traditional image semantics extraction and representation schemes were commonly divided into two categories, namely visual features and text annotations. However, visual feature scheme are difficult to extract and are often semantically inconsistent. On the other hand, the image semantics can be well represented by text annotations. It is also easier to retrieve images according to their annotations. The problem is annotating images are always time-consuming and requiring lots of human effort. In this thesis, we try to develop an automatic image annotation method. We adopted the Growing Hierarchical Self-Organizing Map (GHSOM) to help us discover the concealed relations between image data and annotation data, and annotate image according to such relations. We first applied GHSOM to cluster and generate hierarchies for images and annotations individually. We then proposed a hierarchical mapping method to discover the relations between image clusters and annotation clusters. New images can be annotated according to such relations. We conducted experiments using the proposed method on an image dataset and obtained promising result, which showed that our method could be plausible.