Contextual Hypergraph Modelling for Salient Object Detection
This work addresses the problem of locating attention-grabbing objects in images for computer vision applications, presenting an incremental improvement with new modeling techniques.
The paper tackles salient object detection by modeling images as hypergraphs to capture contextual properties, and also proposes a center-versus-surround contrast analysis method using a cost-sensitive SVM. Experimental results on four datasets show effectiveness against state-of-the-art approaches.
Salient object detection aims to locate objects that capture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this work, we model an image as a hypergraph that utilizes a set of hyperedges to capture the contextual properties of image pixels or regions. As a result, the problem of salient object detection becomes one of finding salient vertices and hyperedges in the hypergraph. The main advantage of hypergraph modeling is that it takes into account each pixel's (or region's) affinity with its neighborhood as well as its separation from image background. Furthermore, we propose an alternative approach based on center-versus-surround contextual contrast analysis, which performs salient object detection by optimizing a cost-sensitive support vector machine (SVM) objective function. Experimental results on four challenging datasets demonstrate the effectiveness of the proposed approaches against the state-of-the-art approaches to salient object detection.