Learning a compact latent representation of the Bag-of-Parts model

The Bag-of-Parts (BoP) model, which employs distinctive parts to represent images, has shown superior performance in vision recognition tasks. Our work is motivated by the need of reducing redundancy in tens of thousands parts. We propose a novel method to learn a compact latent representation from redundant part responses. We address this problem by employing spectral clustering and a multi-column coding scheme. The BoP model is viewed as a multi-scale convolutional model and additional sparse autoencoders are used to infer the latent patterns embedded in high-dimensional part-based representations. Spatial and semantic information is preserved by sparse learning on multiple spatial regions individually. Experiments demonstrate that the learnt representation achieves competitive performance with state-of-the-art methods on PASCAL VOC 2007 dataset.