basic level classes and subordinate class

Comments: the following paper provides a good insight into the role of generative and discriminative models in learning a large number of object categories, i.e., we can use the generative models to distinguish categories at basic level, and discriminative models to differentiate lower-level and similar categories.  

In Subordinate class recognition using relational object models
Aharon Bar-Hillel, Daphna Weinshall, NIPS, 2006, the authors illustrate some interesting points:

"Human categorization is fundamentally hierarchical, where categories are organized in tree-like hierarchies. 

• higher nodes close to the root describe inclusive classes (like vehicles), 
• intermediate nodes describe more specific categories (like motorcycles), 
• lower nodes close to the leaves capture fine distinctions between objects (e.g., cross vs. sport motorcycles).
  

Intuitively one could expect such hierarchy to be learnt either bottom-up or top-down (or both), but surprisingly, this is not the case. In fact, there is a well defined intermediate level in the hierarchy, called basic level, which is learnt first [11]...."

"The primary role of basic level categories seems related to the structure of objects in the world. In [13], Tversky & Hemenway promote the hypothesis that the explanation lies in the notion of parts.Their experiments show that  

• basic level categories (like cars and flowers) are often described as a combination of distinctive parts (e.g., stem and petals), which are mostly unique. 
• higher levels (superordinate and more inclusive) are more often described by their function (e.g., ’used for transportation’), 
• lower levels (sub-ordinate and more specific) are often described by part properties (e.g., red petals) and other fine details."

Based on these assumptions, Bar-Hillel and Weinshall proposed a two stage approach for subordinate class recognition:

1. First we should learn a generative model for the basic category. Using such a model, the object parts should be identified in each image, and their descriptions can be concatenated into an ordered vector. This stage is used to solve the correspondence problem: features in the same entry in two different image vectors correspond since they implement the same part.
   
2. In a second stage, the distinction between subordinate classes can be done by applying standard machine learning tools, like SVM, to the resulting ordered vectors, since the correspondence problem has been solved in the first stage.

Another paper reinforce this idea from the psychology study:  Comparison Processes in Category learning: From Theory to Behavior, Rubi Hammer, Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall and Shaul Hochstein, Brain Research, Special issue on 'Brain and Vision', 2008.