Classification of painting genres based on feature selection

Abstract

In this paper, a painting genre classification system is proposed. Four feature descriptors about the color and texture defined in the MPEG-7 specification, which are more against painting characteristics, are extracted from data sets. Then, we use a self-adaptive harmony search algorithm to select relevant features (or a local feature set) to train each one-against-one SVM classifier. Finally, a majority voting strategy on N(N-1)/2 prediction results would determine their respective genres of paintings. The experimental results show that the overall accuracy reaches 69.8%, and this demonstrates more precise features can be selected for each pair of genres to get better classification results.