An Improved SIFT Feature Extraction Method for Tyre Tread Patterns Retrieval

SIFT features have been found to be effective in describing image textures. Because SIFT features have some great characteristics, such as translation invariance, zooming in and out invariance, spin invariance and affine invariance, etc, so the image retrieval precision is satisfactory usually. However, in Content Based Image Retrieval (CBIR), there are so many SIFT feature points extracted from an image and the size of SIFT-based feature vectors can be up to 128 dimensions. So, even though the prevision based on SIFT feature is high, the retrieval speed is low. To relieve this problem, this paper proposes an improved SIFT feature point extraction method. First of all, taking 2-level wavelet transform to the image, then setting its low-frequency sub-band to zero and reconstructing the image by its 6high frequency sub-bands. The SIFT features are then extracted from the reconstructed 'high-frequency images' for retrieval purpose. This method can reduce the number of SIFT feature points by 71.2%. Tested on a tyre tread pattern dataset, the proposed method is found to be able to significantly improve the retrieval speed while the retrieval precision is still better than other existing methods.