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An interactive system for mental face retrieval

Published: 10 November 2005 Publication History

Abstract

We propose a system to "retrieve" the mental image of a face from a large database using Bayesian inference and relevance feedback. Since the "target image" exists only in the mind of the user, mental image retrieval differs sharply from standard, example-based retrieval and has not been widely studied. In designing the relevance feedback engine, we adopt probabilistic models for the display and answer processes. The answer model is designed to capture properties of human cognition in choosing among displayed faces. The images in each display are selected according to heuristics inspired by maximizing the conditional mutual information between the answer and the target given the previous feedback. Simulations and real tests validate show that the relevance feedback engine operates in real-time and locates the target in a reasonable number of displays.

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cover image ACM Conferences
MIR '05: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
November 2005
274 pages
ISBN:1595932445
DOI:10.1145/1101826
  • General Chairs:
  • Hongjiang Zhang,
  • John Smith,
  • Qi Tian
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 November 2005

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MM&Sec '05
MM&Sec '05: Multimedia and Security Workshop 2005
November 10 - 11, 2005
Hilton, Singapore

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  • (2020)Ultra-wideband Concurrent RangingACM Transactions on Sensor Networks10.1145/340947716:4(1-41)Online publication date: 16-Sep-2020
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