Learning image representations from the pixel level via hierarchical sparse coding

Kai Yu, Yuanqing Lin, John Lafferty
2011 CVPR 2011  
We present a method for learning image representations using a two-layer sparse coding scheme at the pixel level. The rst layer encodes local patches of an image. After pooling within local regions, the rst layer codes are then passed to the second layer, which jointly encodes signals from the region. Unlike traditional sparse coding methods that encode local patches independently, this approach accounts for high-order dependency among patterns in a local image neighborhood. We develop
more » ... s for data encoding and codebook learning, and show in experiments that the method leads to more invariant and discriminative image representations. The algorithm gives excellent results for hand-written digit recognition on MNIST and object recognition on the Caltech101 benchmark. This marks the rst time that such accuracies have been achieved using automatically learned features from the pixel level, rather than using hand-designed descriptors.
doi:10.1109/cvpr.2011.5995732 dblp:conf/cvpr/YuLL11 fatcat:m5m2aikkkveablambe4qwbixy4