Graph regularized GM-pLSA and its applications to video content analysis

Abstract

As standard probabilistic latent semantic analysis (pLSA) is oriented to discrete quantity only, pLSA with Gaussian mixtures (GM-pLSA) succeeding in transferring it to continuous feature space is proposed, which uses Gaussian mixture model to describe the feature distribution under each latent aspect. However, inheriting from pLSA, GM-pLSA still overlooks the intrinsic interdependence between terms, which indeed is an important clue for performance improvement. In this paper, we present a graph regularized GM-pLSA (GRGM-pLSA) model as an extension of GM-pLSA to embed this term correlation information into the process of model learning. Specifically, grounded on the manifold regularization principle, a graph regularizer is introduced to characterize the correlation between terms; by imposing it on the objective function of GM-pLSA, model parameters of GRGM-pLSA are derived via corresponding expectation maximization algorithm. Furthermore, two applications to video content analysis are devised. One is video categorization where GRGM-pLSA serves for feature mapping with two kinds of sub-shot correlations, respectively, incorporated, while the other provides a new perspective on video concept detection, which transforms the detection task to a GRGM-pLSA-based visual-to-textual feature conversion problem. Extensive experiments and comparison with GM-pLSA and several state-of-the-art approaches in both applications demonstrate the effectiveness of GRGM-pLSA.