Density-Based Geometry Validation Method in Image Retrieval

Abstract

The present invention provides a geometric verification method based on density in image matching, comprising the steps of: 1) generating a set of candidate feature matching pairs of two images; 2) for each matching pair in the set of candidate feature matching pairs, estimate its the probability density of the husband space, and use the probability density of the matching pair as the weighting factor of its matching score; similarity. The present invention assigns larger weights to matching pairs located in areas with higher density in the Hough space, and assigns smaller weights to those located in areas with lower density to reflect the possibility of correct matching pairs, and can handle multi-model matching. While retaining the high efficiency advantages of the Hough voting method, it brings greater flexibility.

Description

technical field [0001] The present invention relates to image processing technology. [0002] technical background [0003] Image retrieval has received increasing attention over the past few decades. Since the Bag of Words (BoW) model was introduced into image retrieval in 2003, it has become the most popular image retrieval model due to its efficiency and effectiveness. In the BoW model, the local features are first extracted from the image, such as scale-invariant feature transformation SIFT (Scale-Invariant Feature Transform) features, and then the local feature descriptors are quantized into visual words according to the visual dictionary. It is formed by sub-clustering a large number of local feature descriptors used for training. In this way, images can be represented by frequency histograms of visual words. In the retrieval phase, the database images are ranked according to their similarity measure to the histogram representation of the query image. [0004] Resea...

AI Technical Summary

Problems solved by technology

In order to ensure the principle of one-to-one mapping between feature points, this method adopts an aggressive strategy to remove multiple matches, but this will remove some correct matches and affect the accuracy of matching

In addition, this method couples the process of multiple matching removal and the accumulation of matching scores, which affects the flexibility and efficiency of the algorithm

More importantly, this method does not consider the matching neighbor relationship from the perspective of probability density, which restricts the flexibility and generality of the method

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