  We propose a new fast fully unsupervised method to discover semantic
patterns. Our algorithm is able to hierarchically find visual categories and
produce a segmentation mask where previous methods fail. Through the modeling
of what is a visual pattern in an image, we introduce the notion of "semantic
levels" and devise a conceptual framework along with measures and a dedicated
benchmark dataset for future comparisons. Our algorithm is composed by two
phases. A filtering phase, which selects semantical hotsposts by means of an
accumulator space, then a clustering phase which propagates the semantic
properties of the hotspots on a superpixels basis. We provide both qualitative
and quantitative experimental validation, achieving optimal results in terms of
robustness to noise and semantic consistency. We also made code and dataset
publicly available.
