Indoor object recognition through human interaction using wavelet features

Abstract

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 [8], 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 waveletbased features can efficiently describe the characteristics of different objects in an office-like environment.Comparison with some other feature extraction methods is also given.