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Related Experiment Videos

The uncertain representation ranking framework for concept-based video retrieval.

Robin Aly¹, Aiden Doherty, Djoerd Hiemstra

¹Database Group and Human Media Interaction Group, University of Twente, Enschede, The Netherlands.

Information Retrieval

|October 1, 2013

Summary

This summary is machine-generated.

This study introduces a novel ranking framework for concept-based video retrieval that accounts for uncertain concept detectors. The proposed method enhances search performance by effectively managing detector uncertainty and improving retrieval accuracy.

Keywords:

Concept-based representation Representation uncertainty Video retrieval

Related Experiment Videos

Area of Science:

Computer Science
Information Retrieval
Multimedia Systems

Background:

Concept-based video retrieval systems often struggle with the inherent uncertainty and imperfections of concept detectors.
Existing methods may not adequately address the variability in detector performance, impacting overall retrieval accuracy.

Purpose of the Study:

To propose a general and robust ranking framework for concept-based video retrieval that explicitly addresses concept detector uncertainty.
To enhance the effectiveness of ranking functions by incorporating measures of detector reliability.

Main Methods:

Developed a general ranking framework designed to handle multiple concept-based representations per video segment.
Integrated a weighted combination of expected score (risk-neutral choice) and score standard deviation (risk/opportunity) into the ranking status value.
Leveraged existing text retrieval functions applicable to similar representations.

Main Results:

The proposed framework consistently improved search performance in both shot retrieval and segment retrieval tasks.
Demonstrated superior performance over several baselines across five TRECVid collections.
Validated effectiveness using collections with simulated detectors of varying performance levels.

Conclusions:

The developed ranking framework offers a robust solution for concept-based video retrieval by effectively managing detector uncertainty.
The approach leads to significant improvements in retrieval accuracy and search performance.
The framework's flexibility allows for the integration of various concept detectors and retrieval functions.