Unsupervised Visual Object Categorisation via Self-organisation.

Visual object categorisation (VOC) has become one of the most actively investigated topic in computer vision. In the mainstream studies, the topic is considered as a supervised problem, but recently, the ultimate challenge has been posed: Unsupervised visual object categorisation. Hitherto only a few methods have been published, all of them being computationally demanding successors of their supervised counterparts. In this study, we address this problem with a simple and effective method: competitive learning leading to self organisation (self-categorisation). The unsupervised competitive learning approach is implemented using the Kohonen self-organising map algorithm (SOM). The SOM is used to perform the both unsupervised codebook generation and object categorisation. We present our method in detail and compare results to the supervised approach.