Density-Based Geometry Validation Method in Image Retrieval

An image retrieval and verification method technology, applied in the field of image processing, can solve the problems of affecting the accuracy of matching, restricting flexibility and versatility, affecting the flexibility and efficiency of algorithms, and achieving the effect of flexible framework

Active Publication Date: 2020-07-17
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Density-Based Geometry Validation Method in Image Retrieval
  • Density-Based Geometry Validation Method in Image Retrieval
  • Density-Based Geometry Validation Method in Image Retrieval

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0044] The density-based geometric verification method is applied to image retrieval, and the datasets used are Oxford dataset and Holiday dataset. The Oxford Buildings dataset contains 5062 images downloaded from Flickr. There are 55 query images corresponding to 11 different buildings. Each query image is a rectangular locality representing a building. Relevant results are other images of this building. The Holiday dataset contains 1491 images, which are divided into 500 groups, and each group shows a different scene or object. The first image of each group is used as the query image, and the remaining images are the relevant results of this query image.

[0045] The index used for measuring performance in this embodiment is the general average retrieval accuracy (mAP) in image retrieval, and meanwhile, the retrieval time (seconds) of each image is also measured.

[0046] Implementation steps:

[0047] 1) Use the fast robust feature algorithm SURF to extract the feature...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/583G06K9/46G06K9/62
CPCG06F16/5838G06V10/464G06F18/22G06F18/2415
Inventor 吴洪衡星郑德练徐增林
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap