A robust approach for object recognition

Abstract

In this paper, we present a robust and unsupervised approach for recognition of object categories, RTSI-pLSA, which overcomes the weakness of TSI-pLS A in recognizing rotated objects in images. Our approach uses radial template to describe spatial information (position, scale and orientation) of an object. A bottom up heuristical and unsupervised scheme is also proposed to estimate spatial parameters of object. Experimental results show the RTSI-pLSA can effectively recognize object categories, especially in recognizing rotated, translated, or scaled objects in images. It lowers the error rate by about 10%, compared with TSI-pLS A. Thus, it is a more robust approach for unsupervised object recognition. © Springer-Verlag Berlin Heidelberg 2006.