Integrating Image Clustering and Codebook Learning

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 DL- GMM 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.

Authors

• Pengtao Xie University of California San Diego
• Eric P. Xing

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