Latent Semantic Learning by Efficient Sparse Coding with Hypergraph Regularization

Authors

  • Zhiwu Lu Peking University
  • Yuxin Peng Peking University

Abstract

This paper presents a novel latent semantic learning algorithm for action recognition. Through efficient sparse coding, we can learn latent semantics (i.e. high-level features) from a large vocabulary of abundant mid-level features (i.e. visual keywords). More importantly, we can capture the manifold structure hidden among mid-level features by incorporating hypergraph regularization into sparse coding. The learnt latent semantics can further be readily used for action recognition by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topic models, our sparse coding method with hypergraph regularization can exploit the manifold structure hidden among mid-level features for latent semantic learning, which results in compact but discriminative high-level features for action recognition. We have tested our method on the commonly used KTH action dataset and the unconstrained YouTube action dataset. The experimental results show the superior performance of our method.

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Published

2011-08-04

How to Cite

Lu, Z., & Peng, Y. (2011). Latent Semantic Learning by Efficient Sparse Coding with Hypergraph Regularization. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 411-416. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7896

Issue

Section

AAAI Technical Track: Machine Learning