Multiview Hessian regularized logistic regression for action recognition

TL;DR

This paper introduces a multiview Hessian regularized logistic regression method that effectively leverages multiple data representations and local geometry to improve human action recognition in videos, especially with limited labeled data.

Contribution

The paper proposes a novel multiview Hessian regularized logistic regression model that handles multiple representations and explores local geometry for improved action recognition.

Findings

Effective on USAA dataset with superior accuracy

Leverages multiple representations for better local geometry modeling

Handles multi-view data naturally and efficiently

Abstract

With the rapid development of social media sharing, people often need to manage the growing volume of multimedia data such as large scale video classification and annotation, especially to organize those videos containing human activities. Recently, manifold regularized semi-supervised learning (SSL), which explores the intrinsic data probability distribution and then improves the generalization ability with only a small number of labeled data, has emerged as a promising paradigm for semiautomatic video classification. In addition, human action videos often have multi-modal content and different representations. To tackle the above problems, in this paper we propose multiview Hessian regularized logistic regression (mHLR) for human action recognition. Compared with existing work, the advantages of mHLR lie in three folds: (1) mHLR combines multiple Hessian regularization, each of which obtained from a particular representation of instance, to leverage the exploring of local geometry; (2) mHLR naturally handle multi-view instances with multiple representations; (3) mHLR employs a smooth loss function and then can be effectively optimized. We carefully conduct extensive experiments on the unstructured social activity attribute (USAA) dataset and the experimental results demonstrate the effectiveness of the proposed multiview Hessian regularized logistic regression for human action recognition.