Scalable scene understanding via saliency consensus

Given a single image, we propose a scene understanding framework that segments and categorizes the objects in the scene, and classifies the overall scene. A handful of frameworks already exist to perform these tasks coherently, but training of these models is time-consuming, thereby limiting their scalability. This paper presents a scalable framework by adopting an object-based approach, which sequentially performs unsupervised object discovery using multiple saliency detection algorithms, object segmentation by graph-cut, object classification using the bag-of-features model, and lastly, scene classification by binary decision trees. A novel region-of-interest (ROI) detector, based on morphological image processing techniques, is proposed to automatically provide object location priors from saliency maps. Additionally, for improving object discovery, multiple saliency detectors are combined using a novel method to produce the ROI map, which is then used to obtain the segmentation. We tested our system on a novel object-based scene dataset and obtained a high classification accuracy using the proposed object discovery step. Unlike other existing frameworks, the proposed algorithm maintains scalability due to the fully unsupervised object discovery step, and therefore it can easily accommodate more objects and scene categories.