Abstract
We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex being discriminative or nondiscriminative for a given classification task. To illustrate the applicability and generality of our framework, we use the estimated probabilities as feature scoring to define compact signatures for three different classification tasks: 1) 3D Face Recognition, 2) 3D Facial Expression Recognition, and 3) Ethnicity-based Subject Retrieval, obtaining very competitive results. The main contribution of this work lies in the development of a novel framework for feature selection in scenaria in which the most discriminative information is smoothly distributed along a lattice.
Original language | English (US) |
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Article number | 6205766 |
Pages (from-to) | 728-739 |
Number of pages | 12 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 35 |
Issue number | 3 |
DOIs | |
State | Published - 2013 |
Keywords
- Feature evaluation and selection
- Markov random fields
- face and gesture recognition
- image processing and computer vision
- object recognition
- pattern recognition
- segmentation
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics