Based on low-order overall situation geometry consistency check error match detection method

Abstract

Based on low-order overall situation geometry consistency check error match detection method comprises the steps of using SIFT (scale invariant feature transform) and BoF (bag of features) for extracting and matching feature points in two images to be compared; calculating and combining the square distance matrixes of the two images; dissolving the combined square distance matrix to be a square distance matrix A formed by real matching pairs and a difference matrix E caused by error matching; calculating the sums of elements in each line of the E matrix, sequencing the sums, calculating the second order difference of all sequenced sums, taking the point with the maximum second difference value as a threshold value, and judging the corresponding feature points, higher than the threshold value, of all lines as the error matching pairs; removing the error matching pairs, calculating the similarity between the images according to the real matching points, and outputting an image index result according to the sizes of the similarity through sequencing. The method is simple and efficient, the feature coordinates are used as the only input information, the similarity conversion causing the image difference can be handled, and all error matching pairs can be rightly detected.

Description

technical field [0001] The invention belongs to the field of image retrieval, in particular to the field of partially repeated image retrieval, and relates to a method for detecting erroneous matching points between images. Background technique [0002] In recent years, repeated image search technology, including many search engines including Tineye, Baidu Image Search, and Google Similar Image Search, has developed rapidly. It has many applications in copyright detection, medical diagnosis, violence detection, and geographic information retrieval. Wide range of applications. In this technology, the detection of mismatched feature point pairs between images is one of the key steps. How to use the geometric information between images to correctly filter the mismatches in order to obtain more accurate retrieval results is the core of this technology. . [0003] Partially repeated images mainly refer to pictures taken from different angles of the same scene or pictures before...

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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