Object categorization based on hierarchical learning

In this paper we present a new method for object categorization. Firstly an image representation is obtained by the proposed hierarchical learning method consisting of alternating between local coding and maximum pooling operations, where the local coding operation induces discrimination while the image descriptor and maximum pooling operation induces invariance in hierarchical architecture. Then the obtained effective image representation is passed to a linear classifier which is suitable for large databases for object categorization. We have demonstrated that the proposed method is robust to image variations and has low sample complexity.