Hierarchical Matching Pursuit for RGB-D Recognition

The core of building recognition systems is to extract expressive features from high-dimensional structured data, such as images, depth maps, 3D point clouds, videos and audios. Hierarchical Matching Pursuit (HMP) aims to discover such features from raw sensor data. As a multilayer sparse coding network, HMP builds feature hierarchies layer by layer with an increasing receptive field size to capture abstract features. HMP uses sparse coding to learn codebooks at each layer in an unsupervised way and builds hierarchial feature representations from the learned codebooks using orthogonal matching pursuit, spatial pooling and contrast normalization. HMP achieves state-of-the-art results on many types of recognition tasks. Linear classifiers are sufficient for good accuracy.

Download HMP Source Code.

Download Multipath HMP Source Code.

This work was funded in part by the Intel Science and Technology Center for Pervasive Computing and by ONR MURI grant N00014-07-1-0749.