title: Sparsity-regularized HMAX for visual recognition.
abstract: About ten years ago, HMAX was proposed as a simple and biologically feasible model for object recognition, based on how the visual cortex processes information. However, the model does not encompass sparse firing, which is a hallmark of neurons at all stages of the visual pathway. The current paper presents an improved model, called sparse HMAX, which integrates sparse firing. This model is able to learn higher-level features of objects on unlabeled training images. Unlike most other deep learning models that explicitly address global structure of images in every layer, sparse HMAX addresses local to global structure gradually along the hierarchy by applying patch-based learning to the output of the previous layer. As a consequence, the learning method can be standard sparse coding (SSC) or independent component analysis (ICA), two techniques deeply rooted in neuroscience. What makes SSC and ICA applicable at higher levels is the introduction of linear higher-order statistical regularities by max pooling. After training, high-level units display sparse, invariant selectivity for particular individuals or for image categories like those observed in human inferior temporal cortex (ITC) and medial temporal lobe (MTL). Finally, on an image classification benchmark, sparse HMAX outperforms the original HMAX by a large margin, suggesting its great potential for computer vision.
authors.affiliation: State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), and Department of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Xiaolin
authors.initials: X
authors.lastname: Hu
authors.affiliation: Department of Informatics, University of Hamburg, Hamburg, Germany.
authors.forename: Jianwei
authors.initials: J
authors.lastname: Zhang
authors.affiliation: State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), and Department of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Jianmin
authors.initials: J
authors.lastname: Li
authors.affiliation: State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), and Department of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation; D012372:ROC Curve; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*