  One major branch of saliency object detection methods is diffusion-based
which construct a graph model on a given image and diffuse seed saliency values
to the whole graph by a diffusion matrix. While their performance is sensitive
to specific feature spaces and scales used for the diffusion matrix definition,
little work has been published to systematically promote the robustness and
accuracy of salient object detection under the generic mechanism of diffusion.
In this work, we firstly present a novel view of the working mechanism of the
diffusion process based on mathematical analysis, which reveals that the
diffusion process is actually computing the similarity of nodes with respect to
the seeds based on diffusion maps. Following this analysis, we propose super
diffusion, a novel inclusive learning-based framework for salient object
detection, which makes the optimum and robust performance by integrating a
large pool of feature spaces, scales and even features originally computed for
non-diffusion-based salient object detection. A closed-form solution of the
optimal parameters for the integration is determined through supervised
learning. At the local level, we propose to promote each individual diffusion
before the integration. Our mathematical analysis reveals the close
relationship between saliency diffusion and spectral clustering. Based on this,
we propose to re-synthesize each individual diffusion matrix from the most
discriminative eigenvectors and the constant eigenvector (for saliency
normalization). The proposed framework is implemented and experimented on
prevalently used benchmark datasets, consistently leading to state-of-the-art
performance.
