Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts

This paper proposes a novel approach to constructing a hierarchical representation of visual input that aims to enable recognition and detection of a large number of object categories. Inspired by the principles of efficient indexing (bottom-up), robust matching (top-down), and ideas of compositionality, our approach learns a hierarchy of spatially flexible compositions, i.e. parts, in an unsupervised, statistics-driven manner. Starting with simple, frequent features, we learn the statistically most significant compositions (parts composed of parts), which consequently define the next layer. Parts are learned sequentially, layer after layer, optimally adjusting to the visual data. Lower layers are learned in a category-independent way to obtain complex, yet sharable visual building blocks, which is a crucial step towards a scalable representation. Higher layers of the hierarchy, on the other hand, are constructed by using specific categories, achieving a category representation with a...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Sanja Fidler, Ales Leonardis
• Keywords: Computer Vision, CVPR 2007, Generalizable Parts, Lower Layers, Object Categories, Sharable Visual Building, Small Number