In this paper, we present an object categorization system capable of assigning multiple and related categories for novel objects using multi-label learning. In this system, objects are described using global geometric relations of 3D features. We propose using the Joint SVM method for learning and we investigate the extraction of hierarchical clusters as a higher-level description of objects to assist the learning. We make comparisons with other multi-label learning approaches as well as single-label approaches (including a state-of-the-art methods using different object descriptors). The experiments are carried out on a dataset of 100 objects belonging to 13 visual and action-related categories. The results indicate that multi-label methods are able to identify the relation between the dependent categories and hence perform categorization accordingly. It is also found that extracting hierarchical clusters does not lead to gain in the system's performance. The results also show that using histograms of global relations to describe objects leads to fast learning in terms of the number of samples required for training.

Effective robot manipulation requires a vision system which can extract features of the environment which determine what manipulation actions are possible. There is existing work in this direction under the broad banner of recognising “affordances”. We are particularly interested in possibilities for actions afforded by relationships among pairs of objects. For example if an object is “inside” another or “on top” of another. For this there is a need for a vision system which can recognise such relationships in a scene. We use an approach in which a vision system first segments an image, and then considers a pair of objects to determine their physical relationship. The system extracts surface patches for each object in the segmented image, and then compiles various histograms from looking at relationships between the surface patches of one object and those of the other object. From these histograms a classifier is trained to recognise the relationship between a pair of objects. Our results identify the most promising ways to construct histograms in order to permit classification of physical relationships with high accuracy. This work is important for manipulator robots who may be presented with novel scenes and must identify the salient physical relationships in order to plan manipulation activities.

We present an object recognition system coding shape by view-point invariant geometric relations and appearance. In our intelligent work-cell, the system can observe the work space of the robot by 3 pairs of Kinect and stereo cameras allowing for reliable and complete object information. We show that in such a set-up we can achieve high performance already with a low number of training samples. We show this by training the system to classify 56 objects using Random Forest algorithm. This indicates that our approach can be used in contexts such as assembly manipulation which require high reliability of object recognition.