Indoor object recognition through human interaction using wavelet features

In this paper a preliminary work towards grounded concept learning for a service robot through its vision and human interaction is presented. With a lifelong learning server (LLL), described in [L. S. Lopes and Q.H. Wang, 2002], the robot can incrementally learn to recognize instances of such concepts of indoor objects as "person ", "trash-can " and "triangle sign" using simple intra-band statistical features extracted from the Haar wavelet transform of its vision information under the instruction of a human teacher. Experimental results show that these simple wavelet-based features can efficiently describe the characteristics of different objects in an office-like environment. Comparison with some other feature extraction methods is also given.