Title :
Attribute based object identification
Author :
Yuyin Sun ; Liefeng Bo ; Fox, D.
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Washington, Seattle, WA, USA
Abstract :
Over the last years, the robotics community has made substantial progress in detection and 3D pose estimation of known and unknown objects. However, the question of how to identify objects based on language descriptions has not been investigated in detail. While the computer vision community recently started to investigate the use of attributes for object recognition, these approaches do not consider the task settings typically observed in robotics, where a combination of appearance attributes and object names might be used in referral language to identify specific objects in a scene. In this paper, we introduce an approach for identifying objects based on natural language containing appearance and name attributes. To learn rich RGB-D features needed for attribute classification, we extend recently introduced sparse coding techniques so as to automatically learn attribute-dependent features. We introduce a large data set of attribute descriptions of objects in the RGB-D object dataset. Experiments on this data set demonstrate the strong performance of our approach to language based object identification. We also show that our attribute-dependent features provide significantly better generalization to previously unseen attribute values, thereby enabling more rapid learning of new attribute values.
Keywords :
natural languages; object recognition; pose estimation; robot vision; 3D pose estimation; RGB-D object dataset; attribute based object identification; computer vision; language descriptions; natural language; robotics community; Image color analysis; Materials; Object recognition; Robots; Shape; Sun; Training;
Conference_Titel :
Robotics and Automation (ICRA), 2013 IEEE International Conference on
Conference_Location :
Karlsruhe
Print_ISBN :
978-1-4673-5641-1
DOI :
10.1109/ICRA.2013.6630858