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Efficient image set classification using linear regression based image reconstruction
arXiv, Vol.1701.02485
Cornell University
2017
Abstract
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.
Details
- Title
- Efficient image set classification using linear regression based image reconstruction
- Authors/Creators
- S.A.A. Shah (Author/Creator)U. Nadeem (Author/Creator)M. Bennamoun (Author/Creator)F. Sohel (Author/Creator)R. Togneri (Author/Creator)
- Publication Details
- arXiv, Vol.1701.02485
- Publisher
- Cornell University
- Identifiers
- 991005542308707891
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Other
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