Visual feature extraction using variable map-dimension hypercolumn model

Hypercolumn model (HCM) is a neural network model previously proposed to solve image recognition problem. In this paper, we propose an improved version of HCM network and demonstrate its ability to solve face recognition problem. HCM network is a hierarchical model based on self-organizing map (SOM) that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation. This invariance achieved by alternating between feature extraction and feature integration operation. To improve the recognition rate of HCM, we propose a variable dimension for each map in the feature extraction layer. The number of neurons in each map-side is decided automatically from training data. We demonstrate the performance of the approach using ORL face database.