Object Categorization Using VFA-generated Nodemaps and Hierarchical Temporal Memories

Object categorization and recognition have proved to be difficult tasks in artificial intelligence for several decades. With the recent emergence of biologically inspired soft-computing methods, promising results in specialized application domains are more and more common. In this paper, we propose a novel object categorization method based on statistical properties of nodes -derived from the VFA model -and hierarchical temporal memories. A referential categorization method, obtained by feeding grayscale pixel levels to hierarchical temporal memories, is used to evaluate the model´s performance. Results show that categorization based on the statistics of nodes seems to yield higher success rates. This is in correspondence with Biederman´s conjecture in his theory of recognition by components (RBC), according to which the statistics of nodes, end points and corners carry essential and sufficient information for object recognition [1]. The first section of this paper consists of a brief introduction, in which we restate the formal definition of the VFA model, as well as present its node-filtering applications. This will be followed by a presentation of the HTM theory for size-and orientation-invariant object representation. Finally, we give a detailed case study in which a hierarchical temporal memory is used to distinguish between two, as well as several object categories.