Indexing function-based categories for generic recognition

The authors report the implementation and evaluation of a function-based recognition system that takes an uninterrupted 3-D object shape as its input and reasons to determine if the object belongs to the superordinate category furniture, and if so, into which (sub)category it falls. The system has analyzed over 250 input objects, and the results largely agree with intuitive human interpretation of the objects. The study confirms that a relatively small number of knowledge primitives may be used as the basis for defining a relatively broad range of object categories. The greatest derivation from intuitive human interpretation occurs with objects that humans would not typically label as one of the known categories defined, but which have some novel orientation in which they could serve the function of one of these categories. This is because the system uses a purely function-based definition of the object category.<>