Event Location: NSH 1507
Bio: Pengtao Xie is a graduate student in the Language Technologies Institute, working with Professor Eric Xing. His primary research interests lie in latent space models and large scale distributed machine learning. He received a M.E. from Tsinghua University in 2013 and a B.E. from Sichuan University in 2010. He is the recipient of Siebel Scholarship, Goldman Sachs Global Leader Scholarship and National Scholarship of China.

Abstract: Image clustering and visual codebook learning are two fundamental problems in computer vision and they are tightly related. On one hand, a good codebook can generate effective feature representations which largely affect clustering performance. On the other hand, class labels obtained from image clustering can serve as supervised information to guide codebook learning. Traditionally, these two processes are conducted separately and their correlation is generally ignored. In this paper, we propose a Double Layer Gaussian Mixture Model (DLGMM) to simultaneously perform image clustering and codebook learning. In DLGMM, two tasks are seamlessly coupled and can mutually promote each other. Cluster labels and codebook are jointly estimated to achieve the overall best performance. To incorporate the spatial coherence between neighboring visual patches, we propose a Spatially Coherent DLGMM which uses a Markov Random Field to encourage neighboring patches to share the same visual word label. We use variational inference to approximate the posterior of latent variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of two models.