Fusing generic objectness and visual saliency for salient object detection

• Published in: 2011 International Conference on Computer Vision pp. 914 - 921
• Main Authors: Kai-Yueh Chang, Tyng-Luh Liu, Hwann-Tzong Chen, Shang-Hong Lai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2011
• Subjects: Computational modeling; Detectors; Image edge detection; Object detection; Shape; Visualization
• ISBN: 9781457711015; 145771101X
• ISSN: 1550-5499; 2380-7504
• DOI: 10.1109/ICCV.2011.6126333

We present a novel computational model to explore the relatedness of objectness and saliency, each of which plays an important role in the study of visual attention. The proposed framework conceptually integrates these two concepts via constructing a graphical model to account for their relationships, and concurrently improves their estimation by iteratively optimizing a novel energy function realizing the model. Specifically, the energy function comprises the objectness, the saliency, and the interaction energy, respectively corresponding to explain their individual regularities and the mutual effects. Minimizing the energy by fixing one or the other would elegantly transform the model into solving the problem of objectness or saliency estimation, while the useful information from the other concept can be utilized through the interaction term. Experimental results on two benchmark datasets demonstrate that the proposed model can simultaneously yield a saliency map of better quality and a more meaningful objectness output for salient object detection.