This is the wiki site for the MDS feature learning framework, in which multidimensional scaling (MDS) is applied on high-level pairwise image distances to learn fixed-length vector representations of images. The aspects of the images that are captured by the learned features, which we call MDS features, completely depend on what kind of image distance measurement is employed. With properly selected semantics-sensitive image distances, the MDS features provide rich semantic information about the images that is not captured by other feature extraction techniques.
In our work, we introduce the iterated Levenberg-Marquardt algorithm for solving MDS, and study the MDS feature learning with IMage Euclidean Distance (IMED) and Spatial Pyramid Matching (SPM) distance. We present experiments on both synthetic data and real images — the publicly accessible UIUC car image dataset. The MDS features based on SPM distance achieve exceptional performance for the car recognition task.
- Quan Wang, Kim L. Boyer. Feature Learning by Multidimensional Scaling and its Applications in Object Recognition. 2013 26th SIBGRAPI Conference on Graphics, Patterns and Images (Sibgrapi). IEEE, 2013. [PDF]
The two-stage iterated Levenberg-Marquardt algorithm (ILMA) for solving multidimensional scaling (MDS):
The raw stress vs. running time plot of the SMACOF algorithm, its variants, and the proposed iterated Levenberg-Marquardt algorithm (ILMA) on the Swiss roll geodesic distance matrix:
Flattened Swiss roll surface by applying MDS with iterated Levenberg-Marquardt algorithm on the geodesic distance matrix:
UIUC car recognition performance:
AUTHOR="Quan Wang and Kim L. Boyer",
TITLE="Feature Learning by Multidimensional Scaling and its Applications in Object Recognition",
BOOKTITLE="SIBGRAPI 2013 - Technical Papers",