As one of the fundamental techniques of image processing, image matching is widely used in 3D reconstruction, image retrieval, object tracking and so on. It has attracted lots of attention from computer vision researchers. In the past few decades, much progress has been made in this area. The state-of-art image matching algorithm which is based on excellent local descriptor and epipolar geometry has shown great performance under much circumstance (scale variation, rotation). However, it is not fully invariant to affine transformations. Several affine-invariant image matching algorithms (ASIFT, Fair-Surf) have been proposed in recent years. View simulation is used to overcome affine transformations in these algorithms and achieves great performance. In this paper, a filter mechanism based on global descriptor is introduced into the affine-invariant algorithm and filters most unnecessary matching processes in the original framework. Our approach achieve significant improvement in computation complexity, which is compared to ASIFT’s . In this paper, an algorithm which aims to support accurate image retrieval tasks is proposed. It combines the advantages of image matching techniques and content based image retrieval techniques. The main distributions of this paper are listed as follows: Firstly, a filter mechanism based on global descriptor is introduced into the affine-invariant image matching algorithms. ASIFT exhausts all possible simulated image pairs which are mostly meaningless. Our approach filters most of simulated image pairs which are not similar in appearance by means of computing the distance of global descriptors. The computation complexity of the proposed algortihm is compared to ASIFT’s . Secondly, an algorithm which combines the advantages of image matching techniques and content based image retrieval techniques is proposed in this paper. It can support accurate retrieval tasks in a small image dataset. In the first step, an image set is filtered out by content based image retrieval techniques. Then, the classic image matching algorithm is executed between the query image and every single image in the image set. An accelerate image matching algorithm is also proposed in this paper. Thirdly, based on the algorithm proposed above, a prototype system is implemented. The algorithm was tested in a self-collected image dataset.
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