Coping with Context Change in Open-Ended Object Recognition without Explicit Context Information

To deploy a robot in a human-centric environment, it is important that the robot is able to continuously acquire and update object categories while working in the environment. Therefore, autonomous robots must have the ability to continuously execute learning and recognition in a concurrent or interleaved fashion. One of the main challenges in unconstrained human environments is to cope with the effects of context change. This paper presents two main contributions: (i) an approach for evaluating open-ended object category learning and recognition methods in multi-context scenarios; (ii) evaluation of different object category learning and recognition approaches regarding their ability to cope with the effects of context change. Off-line evaluation approaches such as cross-validation do not comply with the simultaneous nature of learning and recognition. A teaching protocol, supporting context change, was therefore designed and used in this work for experimental evaluation. Seven learning and recognition
approaches were evaluated and compared using the protocol. The best performance, in terms of number of learned categories, was obtained with a recently proposed local variant of Latent Dirichlet Allocation (LDA), closely followed by a Bagof-Words (BoW) approach. In terms of adaptability, i.e. coping with context change, the best result was obtained with BoW, immediately followed by the local LDA variant.