Image classification based on the bagging-adaboost ensemble
Zhiwen Yu, Hau−San Wong · 2008
In order to enable more effective image retrieval via keywords, automatic image annotation and categorization becomes an important problem in computer vision and content based image retrieval. Unfortunately, there exists a semantic gap between the low-level feature vectors and the high-level semantics or concepts. In this paper, we design a basic concept repertory to bridge this semantic gap. Specifically, a basic concept repertory, in the form of a dictionary, is first designed to store a set of classifiers, with each of these representing a concept and a set of rules which is used to distinguish concepts with similar characteristics. We propose a new classifier based on our proposed bagging-adaboosting ensemble (BAE) approach. The experiments demonstrate the good performance of our approaches.