Main subject detection via adaptive feature selection

In this paper we present an algorithm which uses adaptive selection of low-level features for main subject detection. The algorithm first computes low-level features such as contrast and sharpness, each computed in a block-based fashion. Next, the algorithm quantifies the usefulness of each feature by using both statistical and geometric information measured across blocks. Finally, the saliency of each block is determined via a weighted linear combination of the features, where the weights are chosen based on each feature´s estimated usefulness. Our results demonstrate that the adaptive nature of this algorithm allows it to perform competitively with other techniques, while maintaining very low computational complexity.