A Universal Descent Model for Saliency Detection
Han Liu, Bo Li, Tao Zheng, Jiaxu Yao · 2019
We propose an unsupervised method for saliency detection with multiple functions by a universal descent model. The basic descent model is concise, which is a minimization equation conducted by a relationship matrix and background prior. The relationship matrix is calculated by the contrast of features which contains much information between superpixels. So saliency maps with high performance can be obtained even if only one feature is included. Besides, various terms can be appended to the basic descent model to achieve multiple functions. For example, considering an neighbor matrix into the basic model can further smooth saliency maps. The refinement term can also refine existing results by introducing prior saliency maps, demonstrating the universal applications of our model. Extensive experiments on different datasets indicate that the basic model outperforms state-of-the-art methods. The universal descent models with smoothness and refinement terms are able to promote the performance of state-of-the-art methods by a large margin.