Unsupervised Self-Organizing Texture Descriptor

We propose a local texture descriptor based on a pyramidal composition of Self Organizing Map (SOM). As with the SOM model, our visual descriptor presents two operational steps: a first unsupervised learning phase and a second mapping phase involving a dimensionality reduction of the input data. During the first step a large number of image patches, including different classes of textures, are presented to the model. At the end of the learning process the neural weights on each layer of the SOM pyramid will contain good prototypes of the patches used in training at different level of detail. During the mapping phase a new texture patch is presented to the model and, by using a winner take all principle, a winner neuron is selected and its 2D spatial location is used to describe the input patch. Exploiting the topological order of the SOM, two different texture descriptions can be compared using the common Euclidean distance. In the experimental section we show that a simple clustering algorithm like K-means, applied to the local descriptor responses, is able to segment complex texture mosaics with very good results, even in difficult areas like boundaries which separate two different textures.