Exploiting Repetitive Object Patterns for Model Compression and Completion
L.Spinello, R.Triebel, D.Vasquez, K.O.Arras, R.Siegwart
European Conference on Computer Vision 2010 (ECCV)
Keywords: Unsupervised Object Detection, Structure Learning, Model Compression, Conditional Random Fields
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.