Abstract
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize the distance preserving, we propose a novel algorithm called maximum distance embedding (MDE), which aims to maximize the distances between some particular pairs of data points, with the intention of flattening the local nonlinearity and keeping the discriminant information simultaneously in the embedded feature space. Moreover, MDE measures the dissimilarity between data points using L1-norm distance, which is more robust to outliers than widely used Frobenius norm distance. To adapt the nature tensor structure of image and video data, we further propose the multilinear MDE (M2DE). Experiments on various datasets demonstrate that both MDE and M2DE achieve impressive embedding results of image and video data for classification tasks.
Original language | English |
---|---|
Title of host publication | MM'10 - Proceedings of the ACM Multimedia 2010 International Conference |
Pages | 859-862 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2010 |
Event | 18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10 - Firenze, Italy Duration: 25 Oct 2010 → 29 Oct 2010 |
Conference
Conference | 18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10 |
---|---|
Country/Territory | Italy |
City | Firenze |
Period | 25/10/10 → 29/10/10 |
Keywords
- image and video classification
- l1-norm optimization
- manifold learning
- maximum distance embedding
- multilinear maximum distance embedding
- supervised learning
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Software