Regional principal color based saliency detection.
Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
Jing
J
Lou
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
Mingwu
M
Ren
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
Huan
H
Wang
D000465:Algorithms; D001185:Artificial Intelligence; D001288:Attention / Q000502:physiology*; D003116:Color; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D013028:Space Perception / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*