An ordered-patch-based image classification approach on the image Grassmannian manifold

Chunyan Xu, Tianjiang Wang, Junbin Gao, Shougang Cao, Wenbing Tao, Fang Liu

    Research output: Contribution to journalArticlepeer-review

    16 Citations (Scopus)
    8 Downloads (Pure)


    This paper presents an ordered-patch-based image classification framework integrating the image Grassmannian manifold to address handwritten digit recognition, face recognition, and scene recognition problems. Typical image classification methods explore image appearances without considering the spatial causality among distinctive domains in an image. To address the issue, we introduce an ordered-patch-based image representation and use the autoregressive moving average (ARMA) model to characterize the representation. First, each image is encoded as a sequence of ordered patches, integrating both the local appearance information and spatial relationships of the image. Second, the sequence of these ordered patches is described by an ARMA model, which can be further identified as a point on the image Grassmannian manifold. Then, image classification can be conducted on such a manifold under this manifold representation. Furthermore, an appropriate Grassmannian kernel for support vector machine classification is developed based on a distance metric of the image Grassmannian manifold. Finally, the experiments are conducted on several image data sets to demonstrate that the proposed algorithm outperforms other existing image classification methods.
    Original languageEnglish
    Pages (from-to)728-737
    Number of pages10
    JournalIEEE Transactions on Neural Networks and Learning Systems
    Issue number4
    Publication statusPublished - 2014


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