Adaptive Feature Extraction and Image matching Based on Haar Wavelet Transform and SIFT

Recently, Scale Invariant Feature Transform (SIFT) algorithm is widely used in feature extraction and image matching. However, it has some defects, such as large volume of computational data and low efficiency of image matching. To address these defects, adaptive feature extraction and image matching based on Haar Wavelet Transform and SIFT (AHWT-SIFT) is proposed in this paper. In view of the characteristics of Haar wavelet, the low-frequency components of image can be decomposed adaptively by DWT, which represents the main features of the image and avoids the high-frequency of instability redundant information. Then SIFT is applied in these low-frequency components to extract the feature points. Furthermore, nearest neighbor algorithm is utilized for image matching. The experimental results have shown that the proposed scheme not only retains the general characteristics of SIFT, but the speed and accuracy of feature points matching have been greatly improved.