TIGGER: A Texture-Illumination Guided Global Energy Response Model for Illumination Robust Object Saliency

Global saliency is an important aspect of many computerand robotic vision tasks, and with the increased interest infields such as autonomous navigation, a significant area ofresearch. A challenging aspect of modelling global saliencyin practical applications is the presence of varying or non-uniform illumination conditions. Many current models fail toaccurately detect salient regions in non-uniform illuminationconditions and often produce different saliency maps for thesame image under changing illumination. In this paper, wepropose a novel model for illumination robust global saliency. For a given input image, texture-illumination guided energyresponses (TIGERs) are computed at different scales usinga novel multi-scale extension of TIGER. To acquire theseresponses, image intensity is modelled as the summationof the low frequency illumination component and the highfrequency texture component. A captured image is disassociatedinto these components via Bayesian minimization, with the required posterior probability estimated through animportance-weighted Monte Carlo sampling approach. Thetexture-illumination guided global energy response (TIGGER) is computed as the aggregate sum of TIGERs acrossall scales. The global saliency map is obtained via a k-meansclustering-based region adjacency graph (RAG) model. Experimentalresults produce global saliency maps with improvedperformance in non-uniform lighting conditions andgreater consistency when compared to other state-of-the-artmethods.