Image recognition with missing-features based on gaussian mixture model and graph constrained nonnegative matrix factorization

Abstract

The demand for automatically recognizing medical images for screening, reference and management is growing faster than ever. Missing data phenomenon in medical image applications is common existence, and it could be inevitable. In this paper, we have addressed the problem of recognizing medical images with missing-features via Gaussian mixture model (GMM)-based approach. Since training a GMM by directly using high-dimensional feature vectors will result in instability, we have proposed a novel strategy to train the GMM from the corresponding reduced-dimensional one. The proposed method contains training and test phases. The former contains feature extraction, graph constrained nonnegative matrix factorization (NMF), GMM training, and the alternating expectation conditional maximization (AECM) for extending the reduced-dimensional GMM. In test phase, two methods, marginalizing GMM using Bayesian decision (MGBD) and conditional mean imputation (CMI), are applied to impute missing-features. Posterior probability of test images is calculated to identify objects. Experimental results on three real datasets demonstrate the feasibility and efficiency of the proposed scheme.