Efficient Image Set Classification using Linear Regression based Image Reconstruction
This work addresses efficient image set classification for applications like face and object recognition, but it is incremental as it builds on existing subspace methods.
The paper tackles image set classification by using linear regression models to reconstruct test images from gallery subspaces, achieving competitive accuracy and superior computational speed with minimal training data on benchmark datasets.
We propose a novel image set classification technique using linear regression models. Downsampled gallery image sets are interpreted as subspaces of a high dimensional space to avoid the computationally expensive training step. We estimate regression models for each test image using the class specific gallery subspaces. Images of the test set are then reconstructed using the regression models. Based on the minimum reconstruction error between the reconstructed and the original images, a weighted voting strategy is used to classify the test set. We performed extensive evaluation on the benchmark UCSD/Honda, CMU Mobo and YouTube Celebrity datasets for face classification, and ETH-80 dataset for object classification. The results demonstrate that by using only a small amount of training data, our technique achieved competitive classification accuracy and superior computational speed compared with the state-of-the-art methods.