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Related Concept Videos

Position Vectors01:29

Position Vectors

A position vector is a fundamental concept in mathematics that helps determine the position of one point with respect to another point in space. It is a vector that describes the direction and distance between two points. Position vectors are highly useful in the field of math and science, as they help represent spatial relationships and make calculations easier.
For instance, we want to locate a point P(x, y, z) relative to the origin of coordinates O. In that case, we can define a position...
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
The Representativeness Heuristic02:13

The Representativeness Heuristic

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Position and Displacement Vectors01:00

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Biased discriminant euclidean embedding for content-based image retrieval.

Wei Bian1, Dacheng Tao

  • 1School of Computer Engineering, Nanyang Technological University, Singapore. weibian@pmail.ntu.edu.sg

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 4, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces biased discriminative Euclidean embedding (BDEE) and semi-supervised BDEE (semi-BDEE) to enhance content-based image retrieval (CBIR) performance. These methods improve accuracy and stability by considering image feature geometry.

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Content-based image retrieval (CBIR) is crucial for image management and web search.
  • Relevance feedback (RF) algorithms improve CBIR by capturing user preferences and bridging the semantic gap.
  • Existing RF algorithms often overlook the manifold structure of image visual features, limiting performance.

Purpose of the Study:

  • To propose a novel approach, biased discriminative Euclidean embedding (BDEE), to address limitations in current RF algorithms.
  • To develop a semi-supervised version (semi-BDEE) incorporating manifold regularization for unlabelled data.
  • To enhance the accuracy and stability of CBIR systems.

Main Methods:

  • Introduced biased discriminative Euclidean embedding (BDEE) to parameterize samples in high-dimensional space, discovering intrinsic coordinates of visual features.
  • Modeled intraclass geometry and interclass discrimination effectively, avoiding the undersampled problem.
  • Developed semi-BDEE by integrating manifold regularization with BDEE to utilize unlabelled samples.

Main Results:

  • BDEE and semi-BDEE demonstrated significant improvements in accuracy and stability compared to conventional RF algorithms.
  • The proposed methods effectively capture the manifold structure of image low-level visual features.
  • Evaluations were conducted on a subset of the Corel image gallery.

Conclusions:

  • BDEE and semi-BDEE offer a superior approach to relevance feedback in CBIR systems.
  • These methods effectively address the limitations of existing RF algorithms by considering feature geometry.
  • The proposed techniques show strong potential for advancing multimedia applications and web image search.