Exploiting Repetitive Object Patterns for Model Compression and Completion

Many man-made and natural structures consist of similar elements arranged in regular patterns. In this paper we present an unsupervised approach for discovering and reasoning on repetitive patterns of objects in a single image. We propose an unsupervised detection technique based on a voting scheme of image descriptors. We then introduce the concept of latticelets: minimal sets of arcs that generalize the connectivity of repetitive patterns. Latticelets are used for building polygonal cycles where the smallest cycles define the sought groups of repetitive elements. The proposed method can be used for pattern prediction and completion and high-level image compression. Conditional Random Fields are used as a formalism to predict the location of elements at places where they are partially occluded or detected with very low confidence. Model compression is achieved by extracting and efficiently representing the repetitive structures in the image. Our method has been tested on simulated and real data and the quantitative and qualitative result show the effectiveness of the approach.