Template matching is a classical problem in a scene analysis: given a reference image of an object, decide whether that object exists in a scene image under analysis, and find its location if it does. The template matching process involves cross-correlating the template with the scene image and computing a measure of similarity between them to determine the displacement [1]. Since the evaluation of the correlation is computationally expensive, there has been a need for low-cost correlation algorithms for real-time processing. A large number of correlation-type algorithms have been proposed [2]. One of the approaches is to use an image pyramid for both the template and the scene image, and to perform the registration by a top-down search [3]. Other fast matching techniques use two pass algorithms; use a sub-template at a coarsely spaced grid in the first pass, and search for a better match in the neighborhood of the previously found positions in the second pass [4. A. Rosenfeld and A. Kak. Digital Image Processing (2nd Edition, Vol. 2 ed.),, Academic Press, Orlando (1982).4].Afterwards, Jane You[5] presented a wavelet based high performance hierarchical scheme for image matching which includes dynamic detection of interesting points, adaptive thresholding selection and a guided searching strategy for best matching from coarse level to fine level. In order to improve the accuracy of matching and at the same time to reduce the computation load, In this paper, we proposed a robust image matching approach which decreases a large amount of unnecessary searches in contrast to conventional scheme and can achieve a better matching accuracy. Discrete wavelet transform is done firstly on a reference image and a scene image, and low frequency parts of them is extracted, then we use harris corner detection to detect the interesting point in low frequency parts of them to determined the matching candidate region of scene image in reference image, SIFT is used to extracting feature on the matching candidate region and scene image, The extracted features are matched by k-d tree and bidirectional matching strategy to enhance the accuracy of matching. Experiment show that, the algorithm can improve the accuracy of matching and at the same time to reduce the computation load.