A flexible framework of adaptive method selection for image saliency detection

A flexible framework of adaptive method selection is proposed.The approach adaptively selects the best candidate method for new testing instance.It is more efficient than traditional aggregation ways.It yields competitive results on saliency detection datasets. For most of the data analysis tasks (e.g. visual saliency detection), there are usually plenty of candidate methods to be selected. However, it is very difficult to choose a proper one for new instances, especially when the performances of these methods are with little difference overall. Though aggregation strategy aims to take advantage of the different methods, it often has the following weaknesses. Firstly, these methods often tend to combine the results from these candidate methods. Therefore, they suffer from high computation cost. Secondly, the performance may significantly degrade when there are obviously poor results. To address the two limitations above, we propose an instance-aware method selection approach which aims to select a single method instead of aggregating the results of all candidate ones. The proposed approach is based on the following observations: different methods often perform differently and the performance of a method often varies with respect to different instances. Hence, we devise the method selection manner to adaptively choose the best method for a specific instance. We transform the method selection problem into a multi-label annotation problem, which makes it general for many applications and flexible to employ metric learning technique.