Key points in 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.

indexing (bottom-up)
robust matching (top-down)

Category-independent lower layers

Category-specific higher layers

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 small number of highly generalizable parts that gained their structural flexibility through composition within the hierarchy.

This paper proposes a much simpler and efficient learning algorithm, and introduces additional steps that enable a higher level representation of object categories. Additionally, the proposed method is inherently incremental - new categories can be efficiently and continuously added to the system by adding a small number of parts only in the higher hierarchical layers.

Each unit in each hierarchical layer is envisioned as a composition defined in terms of spatially flexible local arrangements of units from the previous layers.

Since the learning process is incremental, categories can be efficiently added to the representation by adding a small number of parts only in the higher hierarchical layers.