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Related Experiment Video

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Automated Charting of the Visual Space of Housefly Compound Eyes

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Published on: March 31, 2022

Flickr distance: a relationship measure for visual concepts.

Lei Wu¹, Xian-Sheng Hua, Nenghai Yu

¹Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China. leiwu@live.com

IEEE Transactions on Pattern Analysis and Machine Intelligence

|October 12, 2011

Summary

This summary is machine-generated.

This study introduces Flickr Distance (FD) to measure visual concept correlation using image data. FD outperforms text-based methods and builds a visual conceptual network (VCNet) for better knowledge representation.

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Area of Science:

Computer Science
Artificial Intelligence
Information Retrieval

Background:

Traditional conceptual distance metrics rely on textual data.
Existing methods like Google Distance (NGD) and Tag Concurrence Distance (TCD) have limitations in measuring conceptual relations accurately.
Scaling conceptual corpora presents challenges for text-based approaches.

Purpose of the Study:

To propose a novel visual distance metric, Flickr Distance (FD), for quantifying the correlation between concepts.
To develop a method that leverages visual information from images for conceptual understanding.
To construct a large-scale visual conceptual network (VCNet) for storing and utilizing conceptual relationships.

Main Methods:

Images related to concepts are collected from Flickr.
A latent topic visual language model (LTVLM) is built to capture various states (views, semantics) of each concept.
Flickr Distance is defined as the Jensen-Shannon (J-S) divergence between the LTVLM of two concepts.

Main Results:

Flickr Distance (FD) effectively measures visual concept correlation using image data.
Methods employing FD significantly outperform NGD and TCD in multimedia tasks.
The constructed Visual Conceptual Network (VCNet) demonstrates coherence with human cognition.

Conclusions:

FD offers a scalable and visually grounded approach to measuring conceptual distances.
FD provides superior performance in real-world applications compared to text-based distances.
The proposed LTVLM and FD contribute to a more robust understanding of visual concept relationships.