An Adaptive Similarity Measure for Browsing Image Databases

Engines for browsing image databases are usually based on predefined fea- tures for selecting the images which are more similar to the one clicked by the user. However, users’ feedbacks and search patterns can be used to gather a set of exam- ples to adapt the feature extraction process in order to reflect the similarity criterion which guides the users’ selections. The proposed approach uses a graph-based image representation that denotes the relationships among regions in the image and a recur- sive neural network which can be trained to extract an optimal feature vector from the graph-based representation. The graph-based representation combines both structural and sub-symbolic features of the image, while the neural network can be trained to map graphs extracted from similar images onto near points in the vectorial feature space. We report a set of preliminary experiments on real world images.