Relevance feedback method based on transfinite learning machine

An ultra-limited learning machine and related feedback technology, which is applied to computer components, special data processing applications, instruments, etc., can solve the problem of reduced processing time and achieve the effects of improved accuracy, good feedback effects, and enriched learning data

Active Publication Date: 2016-03-23
BEIJING UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology uses an algorithm that learns from large amounts of labeled training samples without requiring humans or other agents involved during analysis. It improves efficiency when analyzing complex datasets compared to traditional methods like histograms or fuzzy sets. By incorporating these techniques into this system, it allows for better visualization of high dimensional data patterns through annotation. Overall, this new approach provides technical benefits such as improved image quality and faster processing times.

Problems solved by technology

Technological Problem: Current Content Based Image Retrieval (CBIR) methods have drawbacks due to their slowness or imprecision compared to interactive response systems like LPRS. Specifically, CBIR requires manual annotation of massive datasets containing irrelevant images without being able to fully utilizing them through advanced techniques like deep learning models. Additionally, there may exist issues where annotations made over too many resources result in poor quality output.

Method used

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  • Relevance feedback method based on transfinite learning machine
  • Relevance feedback method based on transfinite learning machine
  • Relevance feedback method based on transfinite learning machine

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Embodiment Construction

[0020] The present invention will be further described below in combination with specific embodiments.

[0021] The flow process of the correlation feedback method based on extreme learning machine described in the present invention is as follows figure 1 shown, including the following steps:

[0022] Step 1, input a query image;

[0023] Step 2: Retrieve the image, obtain the retrieval result, and let the user mark the result;

[0024] Step 2.1, in the retrieval system, what the user needs to mark is the result of a retrieval or the result after a feedback. The pictures marked by the user are used as the data set L, and the pictures not marked by the user are used as the data set U. At this time, the data set not marked by the user is the entire gallery data set D-L. In the labeled data set, there are positive examples P and negative examples N, so L=P∪N.

[0025] Step 3, extract SIFT feature, Color feature, and LBP feature respectively to the marked image;

[0026] Step 4,

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Abstract

The invention relates to a relevance feedback method based on a transfinite learning machine. The relevance feedback method comprises the following steps: inputting an inquiry image; retrieving the image to obtain a retrieval result, and marking a result by a user; respectively extracting an SIFT characteristic, a Colour characteristic and an LBP characteristic from the marked image; training three basis classifiers by utilizing the three kinds of characteristics; respectively putting the image in a retrieval image library in the three basis classifiers, voting according to a prediction result, and automatically marking each unmarked image; training and updating the classifiers again; classifying the image in the image library; and returning a result. The relevance feedback method disclosed by the invention is established on the basis of the transfinite learning machine; human-computer interaction is carried out by introducing the inquiry intention of human beings; learning data are enriched by effectively utilizing the unmarked image in the image library; therefore, the image feedback precision is greatly increased; furthermore, the processing speed is controlled well; expression of the image in a computer accords with understanding of human beings to image semantics well; and thus, the relevance feedback method has a good feedback effect.

Description

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Claims

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Application Information

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Owner BEIJING UNIV OF TECH
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