Semi-automatic image labeling method based on semantic and content

An image annotation and semi-automatic technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of feedback information function, difficult for users to define, low efficiency, etc., and achieve the effect of optimizing semantic vectors

Active Publication Date: 2009-04-29
ZHEJIANG UNIV
View PDF0 Cites 27 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Human labeling has high accuracy but low efficiency; machine labeling has high efficiency but low accuracy
For a content-based image search system, it is generally difficult for users to define these descriptions, and it is also difficult for computer programs to understand these descriptions
In addition, even if the user provides a relatively good initial query, how to make the subseque...

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
  • Semi-automatic image labeling method based on semantic and content
  • Semi-automatic image labeling method based on semantic and content
  • Semi-automatic image labeling method based on semantic and content

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0021] The concrete steps of this embodiment are as follows:

[0022] (1) Build a feedback image retrieval system based on semantics and content. The model of the system is expressed as (F, Q, R(f q , f d )), where F is a set of semantic feature vectors and content feature vectors f of all images in the database, Q is the feature set of image semantic and content information requirements expressed by users, R(f q , f d )) formula is to calculate f q ∈Q, f d The similarity obtained by ∈F is an arrangement function arranged from large to small according to the similarity; the content feature vector uses color feature vector and texture feature vector, that is, color consistency vector (CCV) and Gabor filter vector. Among them, the similarity comparison algorithm uses three methods: chi-square test, JD separation and Euclidean.

[0023] (2) The user submits a keyword-based query, such as "pizza tower, sky, grass", which means that the user wants to get a panorama of the Pisa...

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 discloses a semi-automatic image tagging method based on semantics and content. The tagging method comprises the following steps: a feedback image retrieval system based on semantics and content is established; a model of the system is expressed as (F, Q, R (fq, fd)) with a triple; a user submits an query based on keywords; the system converts a keyword sequence into a group of semantic characteristics vector Omega i', uses an arrangement function R (fq, fd)) for doing an query based on semantics for all the images in a database, and returns the query result to the user according to the arrangement; the user chooses relatively satisfactory images from the query result and feeds back to the system; the system again uses the arrangement function R (fq, fd)) for doing the query based on semantics or content or integrating the semantics and the content for all the images in a database, finds out all the images which are similar to the images that the user is satisfied with, and simultaneously adjusts the weight of the tagging information that each image corresponds to. The invention has the advantages of high efficiency, high accuracy, and friendly interactive way.

Description

technical field [0001] The invention relates to a computer image search and image labeling method, in particular to a semi-automatic image labeling method based on semantics and content. Background technique [0002] In the past ten years, with the rapid development of computer network technology, popularization and application, and a sharp decline in data storage costs, the use of multimedia databases has become more and more common. Multimedia information presented in the form of images, music, and video plays an increasingly important role in both business and entertainment. How to effectively manage and organize such a large amount of data, and search out the information that users need has become a very important issue. There has been a long history of research on browsing, searching and indexing technologies for multimedia data, especially image data. Today, the topic of image databases and visual information search has become the most active part of the research fie...

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): G06F17/30
Inventor 吴朝晖郑清照丁艳春姜晓红
Owner ZHEJIANG 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