Learning Statistical Structure for Object Detection

Abstract. Many classes of images exhibit sparse structuring of statistical dependency. Each variable has strong statistical dependency with a small number of other variables and negligible dependency with the remaining ones. Such structuring makes it possible to construct a powerful classifier by only representing the stronger dependencies among the variables. In particular, a seminaïve Bayes classifier compactly represents sparseness. A semi-naïve Bayes classifier decomposes the input variables into subsets and represents statistical dependency within each subset, while treating the subsets as statistically independent. However, learning the structure of a semi-naïve Bayes classifier is known to be NP complete. The high dimensionality of images makes statistical structure learning especially challenging. This paper describes an algorithm that searches for the structure of a semi-naïve Bayes classifier in this large space of possible structures. The algorithm seeks to optimize two ...

• Type: Conference
• Year: 2003
• Where: CAIP
• Authors: Henry Schneiderman
• Keywords: CAIP 2003, Semi-naïve Bayes, Statistical Dependency, Strong Statistical Dependency