Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
This work addresses multi-view face recognition for computer vision applications, but it is incremental as it builds on existing sparse representation and prior frameworks.
The paper tackled multi-view face recognition by extending class-specific spike-and-slab priors to multitask scenarios, demonstrating improved performance in low training scenarios through joint information mining from different camera views.
Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of \emph{class-specific} spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.