Ground-Truth Data Set and Baseline Evaluations for Base-Detail Separation Algorithms at the Part Level

Base-detail separation is a fundamental image processing problem, which models the image by a smooth base layer for the coarse structure and a detail layer for the texturelike structures. Base-detail separation is hierarchical and can be performed from the fine level to the coarse level. The separation at coarse level, in particular at the part level, is important for many applications, but currently lacks ground-truth data sets that are needed for comparing algorithms quantitatively. Thus, we propose a procedure to construct such data sets and provide two examples: Pascal Part UCLA and Fashionista, containing 1000 and 250 images, respectively. Our assumption is that the base is piecewise smooth, and we label the appearance of each piece by a polynomial model. The pieces are objects and parts of objects obtained from human annotations. Finally, we propose a way to evaluate different separation methods with our data sets and compared the performances of seven state-of-the-art algorithms.