Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evidence on Skill Differences of Women and Men Concerning Face Recognition
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Face Recognition Algorithms as Models of Human Face Processing
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Journal of Cognitive Neuroscience
IEEE Transactions on Image Processing
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
An interactive system for mental face retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Feature synthesized EM algorithm for image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A bi-objective optimization model for interactive face retrieval
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Iterative relevance feedback with adaptive exploration/exploitation trade-off
Proceedings of the 21st ACM international conference on Information and knowledge management
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We propose a relevance feedback system for retrieving a mental face picture from a large image database. This scenario differs from standard image retrieval since the target image exists only in the mind of the user, who responds to a sequence of machine-generated queries designed to display the person in mind as quickly as possible. At each iteration the user declares which of several displayed faces is “closest” to his target. The central limiting factor is the “semantic gap” between the standard intensity-based features which index the images in the database and the higher-level representation in the mind of the user which drives his answers. We explore a Bayesian, information-theoretic framework for choosing which images to display and for modeling the response of the user. The challenge is to account for psycho-visual factors and sources of variability in human decision-making. We present experiments with real users which illustrate and validate the proposed algorithms.