Object Categorization Using Biologically Inspired Nodemaps and the HITEC Categorization System
Adam Csapo, Barna Resko, Domonkos Tikk, and Peter Baranyi
Budapest Univ. of Technology and Economics, H-1111, Budapest, Muegyetem Rkp. 3-9, Hungary
Computer and Automation Research Institute, Hungarian Academy of Sciences, H-1111, Budapest, Kende u. 13-17, Hungary
The computerized modeling of cognitive visual information has been a research field of great interest in the past several decades. The research field is interesting not only from a biological perspective, but also from an engineering point of view when systems are developed that aim to achieve similar goals as biological cognitive systems. This paper briefly describes a general framework for the extraction and systematic storage of low-level visual features, and demonstrates its applicability in image categorization using a linear categorization algorithm originally developed for the characterization of text documents. The performance of the algorithm together with the newly developed feature array was evaluated using the Caltech 101 database. Extremely high (95% and higher) success rates were achieved when distinguishing between pairs of categories using independent test images. Efforts were made to scale up the number of categories using a hierarchical, branch-and-bound decision tree, with limited success.
-  I. Biederman, “Recognition-by-Components: A Theory of Human Image Understanding,” Psychological Review, 94, pp. 115-147, 1987.
-  D. Lowe, “Object Recognition from Local Scale-Invariant Features,” In Proc. of the Int. Conf. on Computer Vision, pp. 1150-1157, 1999.
-  A. Berg and J. Malik, “Geometric Blur for Template Matching,” In IEEE Conf. on Computer Vision and Pattern Recognition, pp. 607-614, 2001.
-  M. Riesenhuber and T. Poggio, “Robust Object Recognition with Cortex-Like Mechanisms,” Nature, 11, pp. 1019-1025, 1999.
-  T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, “Robust Object Recognition with Cortex-Like Mechanisms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, pp. 411-426, 2007.
-  D. Tikk, G. Biro, and J. Yang, “Applied Research in Uncertainty Modeling and Analysis,” chapter Experiments with a hierarchical text categorization method on WIPO patent collections, pp. 283-302, Int. Series in Intelligent Technologies, No.20, Springer-Verlag, 2005.
-  A. Csapo, P. Baranyi, and D. Tikk, “Object Categorization Using VFA-generated Nodemaps and Hierarchical Temporal Memories,” In Proc. of IEEE Int. Conf. on Computational Cybernetics, pp. 257-262, 2007.