Density-based geometrical calibration method in image retrieval

Abstract

The invention provides a density-based geometrical calibration method in image matching. The method comprises the steps of: (1) generating a candidate feature matching pair set of two images; (2) estimating a probability density of each matching pair in the candidate feature matching pair set in a Hough space, and taking the probability density of each matching pair as a weighting factor of a matching score of the matching pair; and (3) accumulating the matching scores of all feature matching pairs in a feature matching pair set to obtain the matching score between the images, as a similarity between the two images. According to the method, a large weight is endowed to the matching pair located at an area with a high density in the Hough space, a small weight is endowed to the matching pair located at the area with a low density in the Hough space so as to reflect the correct probability of the matching pairs, the multi-model matching can be processed, and great flexibility is brought while maintaining the advantage of high efficiency of a Hough voting method.

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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