Multi-criteria Energy Minimization with Boundedness, Edge-density and Rarity, for Object Saliency in Natural Images

Recent methods of bottom-up salient object detection have attempted to either: (i) obtain a probability map with a `contrast rarity' based functional, formed using low level cues; or (ii) Minimize an objective function, to detect the object. Most of these methods fail for complex, natural scenes, such as the PASCAL-VOC challenge dataset which contains images with diverse appearances, illumination conditions, multiple distracting objects and varying scene environments. We thus formulate a novel multi-criteria objective function which captures many dependencies and the scene structure for correct spatial propagation of low-level priors to perform salient object segmentation, in such cases. Our proposed formulation is based on CRF modeling where the minimization is performed using graph cut and the optimal parameters of the objective function are learned using a max-margin framework from the training set, without the use of class labels. Hence the method proposed is unsupervised, and works efficiently when compared to the very recent state-of-the art methods of saliency map detection and object proposals. Results, compared using F-measure and intersection-over-union scores, show that the proposed method exhibits superior performance in case of the complex PASCAL-VOC 2012 object segmentation dataset as well as the traditional MSRA-B saliency dataset.