TY  - CONF
AU  - Miguel Oliveira
AU  - L. Seabra Lopes
AU  - G. Hyun Lim
AU  - S. Hamidreza Kasaei
AU  - Angel Sappa
AU  - A. Tom
A2  - IROS
PY  - 2015//
TI  - Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains
BT  - International Conference on Intelligent Robots and Systems
SP  - 2488
EP  - 2495
KW  - Visual Learning
KW  - Computer Vision
KW  - Autonomous Agents
N2  - In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks.
UR  - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7347169
L1  - http://refbase.cvc.uab.es/files/OSL2015.pdf
UR  - http://dx.doi.org/10.1109/IROS.2015.7353715
N1  - ADAS; 600.076
ID  - Miguel Oliveira2015
ER  -