  Appearance-based generic object recognition is a challenging problem because
all possible appearances of objects cannot be registered, especially as new
objects are produced every day. Function of objects, however, has a
comparatively small number of prototypes. Therefore, function-based
classification of new objects could be a valuable tool for generic object
recognition. Object functions are closely related to hand-object interactions
during handling of a functional object; i.e., how the hand approaches the
object, which parts of the object and contact the hand, and the shape of the
hand during interaction. Hand-object interactions are helpful for modeling
object functions. However, it is difficult to assign discrete labels to
interactions because an object shape and grasping hand-postures intrinsically
have continuous variations. To describe these interactions, we propose the
interaction descriptor space which is acquired from unlabeled appearances of
human hand-object interactions. By using interaction descriptors, we can
numerically describe the relation between an object's appearance and its
possible interaction with the hand. The model infers the quantitative state of
the interaction from the object image alone. It also identifies the parts of
objects designed for hand interactions such as grips and handles. We
demonstrate that the proposed method can unsupervisedly generate interaction
descriptors that make clusters corresponding to interaction types. And also we
demonstrate that the model can infer possible hand-object interactions.
