Probabilistic modeling of scenes using object frames

In this paper, we propose a probabilistic scene model using object frames, each of which is a group of co-occurring objects with fixed spatial relations. In contrast to standard co-occurrence models, which mostly explore the pairwise co-existence of objects, the proposed model captures the spatial relationship among groups of objects. Such information is closely tied to the semantics of the underlying scenes, which allows us to perform object detection and scene recognition in a unified framework. The proposed probabilistic model has two major components. The first models the dependencies between object frames and objects by adopting the Latent Dirichlet Allocation model for text analysis. The second component characterizes the dependencies between object frames and scenes by establishing a mapping between global image features and object frame distributions. Experimental results show that the induced object frames are both semantically meaningful and spatially consistent. In addition, our model significantly improves the performance of object recognition and scene retrieval.