Feature extraction and reduction strategy based on pyramid HOG and hierarchal exploitation of cortex-like mechanisms

In this work we propose a hierarchal feature extraction and reduction framework for object recognition. This framework is motivated by both primate visual cortex and ensemble classification based techniques for object recognition. By understanding the representation of the objects from edges based features, we select PHOG (pyramid histogram of oriented gradients) to describe an object. The curse of PHOG feature dimensionality has been addressed by using a technique which not only reduces dimensions of features, but this feature reduction technique also follows ensemble classification and the hierarchal theory of object recognition in ventral stream of primate visual cortex. Technique follows the feed forward models of object recognition in cortex but is less computationally expensive as compared to other cortex-like mechanisms in literature. We demonstrate the performance of proposed technique over Caltech, MIT-CBCL and INRIA datasets.