Density-based geometrical calibration method in image retrieval

A technology of image retrieval and verification methods, applied in the field of image processing, can solve problems affecting the accuracy of matching, affecting the flexibility and efficiency of algorithms, restricting flexibility and versatility, and achieving the effect of flexible framework
CN106547867AActive Publication Date: 2017-03-29UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
UNIV OF ELECTRONICS SCI & TECH OF CHINA
Publication Date
2017-03-29

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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.
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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...

Claims

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