Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Self-adaptation Hash rearrangement method for image retrieval

An image retrieval and self-adaptive technology, applied in special data processing applications, instruments, electrical digital data processing, etc.

Inactive Publication Date: 2013-07-31
DALIAN UNIV OF TECH
View PDF3 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The purpose is to solve the sorting problem of returned images in massive image retrieval, especially effectively solve the sorting problem of images with equal distances in returned images, and improve the accuracy of retrieval

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
  • Self-adaptation Hash rearrangement method for image retrieval
  • Self-adaptation Hash rearrangement method for image retrieval
  • Self-adaptation Hash rearrangement method for image retrieval

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0029] Example 1, the image library contains 10,000 color images of 100×100 pixels, a total of 100 categories, 100 images in each category, from the “Product Image Categorization DataSet” commodity image library established by Microsoft Asia Research Institute Xie Xing et al. Recognized image library on the Internet.

[0030] Step 1. Randomly take 1000 images from the image library as the retrieval image q, and the remaining 9000 images are used as the training library.

[0031] Step 2. Convert all color images in the retrieved image and training database into grayscale images, and extract 512-dimensional gist visual features. Image feature library and training feature library are GIM={GIM 1 ,GIM 2 ,...,GIM 10000} and GT={GT 1 , GT 2 ,...,GT 9000},in Among them, the extraction process of gist features can use the public matlab code.

[0032] Step 3. Using the public matlab code, select the three commonly used hashing methods LSH, SKLSH and ITQ to train the feature l...

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 belongs to the technical field of image retrieval, and relates to a self-adaptation Hash rearrangement method for image retrieval, in particular to an image Hash method for carrying out content-based image retrieval. The method adopts the mapping and then sequencing Hash rearrangement method, and comprises the following steps: high-dimensional visual feature vectors of images in a training library are abstracted, a proper Hash method is selected to map the high-dimensional visual feature vectors into Hash codes, and specific class weight vectors are generated for each class of images; the hamming distance between the Hash codes of the retrieved images and the Hash codes in the training library is calculated, and retrieval results are returned in an ascending order; and according to the retrieval results, the self-adaptation weight vectors of the retrieved images are calculated, the hamming distance is weighted by utilizing the structures of the self-adaptation weight vectors of the retrieved images, the returned images are rearranged by utilizing the weighted hamming distance, and more accurate retrieval results are obtained. The self-adaptation Hash rearrangement method calculates specific weights according to the retrieved images, has universality, and remarkably improves the retrieval effect without increase of calculation complexity.

Description

technical field [0001] The invention belongs to the technical field of image retrieval, relates to content-based image retrieval using an image hash method, and in particular to an image retrieval-oriented self-adaptive hash rearrangement method. Background technique [0002] Content-based image retrieval uses the similarity between visual features of images as a metric for retrieval, and its task is to find images similar in content to the retrieved image from the image database. Traditional retrieval methods use high-dimensional Euclidean feature vectors to represent images, and use linear scanning to retrieve image databases. However, when retrieving massive image databases, due to the large number of images and the corresponding feature storage space, the retrieval efficiency of the linear scanning method is very low. The image hashing method maps the high-dimensional Euclidean features into concise binary hash codes, which greatly reduces the storage space of the featu...

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 Applications(China)
IPC IPC(8): G06F17/30
Inventor 孔祥维卢佳音付海燕
Owner DALIAN UNIV OF TECH
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
Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com