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We present a novel feature descriptor, Local Polar DCT Features (LPDF), which is robust to a variety of image transformations. Specifically, the local patch is quantized in the designed polar geometric structure and the 2-D DCT features are then extracted and rearranged. A subset of the resulting DCT coefficients is selected as our compact LPDF descriptor. We perform a comprehensive performance evaluation with state-of-the-art methods, i.e., SIFT, DAISY, LIOP, and GLOH on the standard Oxford dataset and two additional test image pairs. Experimental results demonstrate the superiority of proposed descriptor under various image transformations, even with very low dimensions.