Image annotation techniques based on feature selection for class-pairs
Image annotation technique can be formulated as a multi-class classification problem, which can be solved by the ensemble of multiple class-pair classifiers. Support vector machine (SVM) classifiers based on optimal class-pair feature subsets from the multimedia content description interface (MPEG-7) standard are used as the class-pair classifiers. We use a binary-coded chromosome genetic algorithm (GA) to select optimal class-pair feature subsets, and a bi-coded chromosome GA to simultaneously select optimal class-pair feature subsets and corresponding optimal weight subsets, i.e. optimal class-pair weighted feature subsets. We consider two kinds of methods for class-pair feature selection: a common optimal (or weighted) feature subset is selected for all the class-pairs, and an individual optimal (or weighted) feature subset is selected for each class-pair respectively. Majority voting scheme is used to combine the class-pair SVM classifiers. The experiments are performed on two different image sets to validate the performance of our image annotation techniques.
image annotation feature selection support vector machine multimedia content description interface genetic algorithm