Automatic function selection for large scale salient object detection
Yuli Gao, Jianping Fan · 2006
Robust detection of a large dictionary of salient objects in natural image database is of fundamental importance to image retrieval systems. We review three popular frameworks for salient object detection, i.e., segmentation-based method, grid-based method and part-based method and discuss their advantages and limitations. We argue that using these frameworks individually is generally not enough to handle a large number of salient object classes accurately because of the intrinsic diversity of salient object features. Motivated by this observation, we have proposed a new system which combines the merits of these frameworks into one single hybrid system. The system automatically selects the appropriate modeling method for each individual object class using J measure and shape variance. We conduct comparison experiments on two popular image dataset – Corel and LabelMe. Empirical results have shown that the proposed hybrid method is more general and can handle much more salient object classes in a robust manner.