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Related Experiment Video

Updated: Jun 22, 2026

Training Synesthetic Letter-color Associations by Reading in Color

Training Synesthetic Letter-color Associations by Reading in Color

Published on: February 20, 2014

Learning color names for real-world applications.

Joost van de Weijer¹, Cordelia Schmid, Jakob Verbeek

¹Computer Vision Center, Barcelona, Spain. joost@cvc.uab.es

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 2, 2009

Summary

This summary is machine-generated.

Color names learned from real-world images significantly outperform those from traditional color chips for image retrieval and annotation. This study used noisy web data and probabilistic latent semantic analysis (PLSA) models.

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Area of Science:

Computer Vision
Natural Language Processing
Machine Learning

Background:

Color names are crucial for real-world applications like image retrieval and annotation.
Traditional methods rely on labeled color chips from controlled experiments.
Real-world image color naming presents unique challenges compared to experimental settings.

Purpose of the Study:

To compare color names learned from traditional color chips versus real-world images.
To develop methods for learning color names from noisy, unlabeled real-world image data.
To evaluate the effectiveness of learned color names in image retrieval and annotation tasks.

Main Methods:

Collected a dataset of real-world images using Google Image, acknowledging inherent noise and mislabeling.
Proposed and adapted several variants of the Probabilistic Latent Semantic Analysis (PLSA) model to handle noisy data.
Evaluated the performance of learned color names on image retrieval and image annotation tasks.

Main Results:

Color names derived from real-world images demonstrated superior performance compared to those learned from color chips.
The proposed PLSA variants effectively learned color names from noisy image datasets.
Significant improvements were observed in both image retrieval and annotation tasks using real-world learned color names.

Conclusions:

Learning color names from real-world images, even with noisy data, yields better results than traditional methods.
The PLSA model is a viable approach for extracting meaningful color names from large-scale, imperfect datasets.
This research advances the application of color naming in practical computer vision tasks.