Similar image retrieval method based on local feature neighborhood information

A technology of local features and neighborhood information, applied in still image data retrieval, metadata still image retrieval, computer parts, etc., can solve the problems of low feature matching accuracy, low computational efficiency, feature locality and quantization error, etc. , to achieve the effect of enhancing visual matching, high computing efficiency, and improving accuracy

Active Publication Date: 2014-12-10
TONGJI UNIV
View PDF3 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] First, due to the locality of features and quantization errors, the feature matching accuracy is very low, and there are a large number of wrong matches, which affect the final retrieval accuracy
[0006] Second, a large number of algorithms focus on the information of the feature points themselves, ignoring the strong co

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
  • Similar image retrieval method based on local feature neighborhood information
  • Similar image retrieval method based on local feature neighborhood information
  • Similar image retrieval method based on local feature neighborhood information

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0052] like figure 1 and figure 2 As shown, the similar image retrieval method based on local feature neighborhood information provided by the present invention includes two parts: offline training and online retrieval. like figure 1 As shown, the specific process of offline training is:

[0053] Step S101, obtain the training picture, use the Hessian-Affine feature point detection algorithm and the SIFT local feature descriptor to perform feature detection and description on the picture in the multi-scale space, specifically:

[0054]a) Use the Hessian-Affine feature point detection algorithm to image I i Perform detection to obtain the corresponding local feature point set P i ={p i，1 ,...,p i，m}, i=1, 2, ... n, n is the total number of pictures, and m is the number of local feature points in each picture;

[0055] b) Using SIFT lo...

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 invention relates to a similar image retrieval method based on local feature neighborhood information. The method includes the steps of (1) obtaining training pictures, (2) conducting feature detection and description on the pictures in a multi-scale space through a Hessian-Affine feature point detection algorithm and an SIFT local feature descriptor, (3) constructing corresponding shadow features according to features extracted in the step (2), (4) conducting clustering on the features extracted in the step (2) through a k mean value clustering algorithm and generating a visual dictionary including k visual words, (5) mapping all the features to the visual word with the smallest distance with L2 of the visual word one by one and storing the features in an inverted index structure, (6) storing the inverted index structure to form a query database and (7) obtaining the corresponding inverted index for querying the pictures, comparing the inverted index with the query database and obtaining a retrieval result list. Compared with the prior art, the method has the advantages of being high in picture retrieval accuracy.

Description

technical field [0001] The invention relates to a picture retrieval method, in particular to a similar picture retrieval method based on local feature neighborhood information. Background technique [0002] In recent years, computer vision has been developing rapidly. Among them, similar image retrieval is a basic but challenging task, so it has attracted much attention. [0003] Currently, the bag-of-words model based on local features and inverted index structure is one of the most commonly used image retrieval models. Image local features are a type of features used in the field of image processing. Finding extreme points in the scale space, extracting position, scale, and rotation invariants can detect key points in the image. The bag-of-words model is an approximate method for feature matching. In this model, a local feature is quantized to its nearest visual word in a pre-trained dictionary and stored in an inverted index for querying. [0004] However, the above i...

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
IPC IPC(8): G06F17/30G06K9/66
CPCG06F16/58G06F16/583G06V30/194
Inventor 王瀚漓王雷
Owner TONGJI UNIV
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