L1 norm total geometrical consistency check-based wrong matching detection method

Abstract

The invention discloses an L1 norm total geometrical consistency check-based wrong matching detection method, which comprises the steps: SIFT (scale-invariant feature transform) and a bag-of-features model are used for extracting and matching characteristic points of two images; the squared distance matrix of the two images is calculated; a golden section search method is used for solving: min[Lambda is larger than 0]||D[1]-Lambda.D[2]||[1]; the difference matrix E is calculated, wherein E=||D[1]-Lambda*.D[2]||[1]; the sum of all elements in each row in the matrix E is calculated, the sums are sequenced from large to small, the second order difference of the sequenced sums is calculated, the point with the maximum second order difference value is taken as a threshold value, and characteristic points which correspond to all rows and are higher than the threshold value are determined as wrong matching pairs; after the wrong matching pairs are removed, the similarity of the images is calculated according to the true matching points of the two images, and then an image search result is output according to the similarity from large to small. According to the detection method disclosed by the invention, complicated backgrounds, partly shielding and various similar geometrical transformation among the similar images are allowed; only the coordinate information of characteristic points is utilized, and the detection method is simple and efficient.

Description

Technical field [0001] The invention belongs to the field of image retrieval, especially in the field of partially repeated image retrieval, a method for detecting wrong matching points between images and images. Background technique [0002] In recent years, the repeated image search technology of many search engines, including Tineye, Baidu Zhitu, and Google similar image search, has developed rapidly. Its areas of copyright detection, medical diagnosis, violence detection, and geographic information retrieval, etc. Wide range of applications. In this technology, the detection of mismatched feature point pairs between images is a key step. How to use geometric information between images to correctly filter mismatches in order to obtain more accurate retrieval results is the core of this technology . [0003] Partially repeated images mainly refer to the pictures taken from different angles of the same scene or the pictures before and after processing by image processing softwar...

AI Technical Summary

Problems solved by technology

J.Philbin et al. [4] proposed to apply the classic Random Sampling Consistency (RANSAC) algorithm to deal with the problem of mismatch detection under the perspective transformation model, but using RANSAC will lead to a large calculation time-consuming, so it is not suitable for large-scale search questions under

Another way of thinking is the geometric coding (GC) method proposed by Wengang Zhou et al. [5]. This method first encodes the mutual position information and rotation transformation information of feature points in each image, and then compares the The encoding difference of the feature points is used to detect the wrong matching points. Since the scale and main direction information of the feature points are used, this method is still time-consuming.

The feature of the global method is that the detection effect is better, and it can adapt to more complex geometric transformation models. The disadvantage is that it consumes too much time and is not conducive to the application background of large-scale retrieval.

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