An Improved Image Matching Algorithm Based on SIFT

For matching visible image with many similar regions, the original image matching algorithm based on SIFT (scale invariant feature transform) has the disadvantages of limited matching constraints, high false matching rate and difficulty to effectively remove mismatching points. To overcome the shortcomings above, an improved algorithm was proposed in which a combined measure of distance similarity matching with cosine similarity matching was adopted to dealing with 128-dimensional feature vectors. Further, the orientation consistency of the image feature points was employed to reduce the false matching rate. Experimental results show that the proposed algorithm has a good matching result on the conditions of image zooming, rotating, lighting, noising and small-scale perspective transformation. Compared with the original algorithm, based on the premise of ensuring enough matching points and definite matching time, the improved algorithm achieves a 10% to 20% average reduction of the false matching rate for images zooming, rotating, lighting, noising transformation and 5% for small-scale perspective transformation.