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Manifold Preserving Features and Regression for Semantic Labelling in High Dimensional Images

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Abstract

This paper proposes an combined method for manifold preservation and Subspace Eigenvectors(SE) based regression in high dimensional (HD) images. We studied semantic labelling of HD images by using regression where partially labelled samples are provided. The first phase is rest on a weighted graph based Subspace operation which properly creates and fuses the external knowledge of labelled information. In earlier literature, edge weight matrix create the Laplace graph from quite high dimensional dataset that includes spectral information. An addition to Laplace Eigenvector method, an other method that includes spatial knowledge is termed as Subspace Eigenvectors(SE). Afterwards, abbreviated data set is projected in identified subspaces with a Bayesian approach having partially labelled training samples. The proposed method is a fusion of manifold subspace with regression for pixel diagnostics based semantic labelling and the provisional study of other predictive machine learning algorithms(MLA’s) that are trained with limited number of labelled training sets and are tested on immense amount of data. The results demonstrate that our analysis is clearly revealing the complicated behaviour of high dimension(HD) image experience in lower dimensions.

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Acknowledgements

We would like to thank shai bagon for providing Graphcut (GC) optimization based C++ wrapper libraries. Open-source image data-sets are obtained from [Aviris-NASA (JPL); Gamba (Accessed: 2019-01-20)].

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bennett University, Greater Noida, India

    Vishal Srivastava

  2. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India

    Shashank Sheshar Singh

  3. Department of Computer Science and Engineering, IIT-BHU, Varanasi, India

    Bhaskar Biswas

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  1. Vishal Srivastava
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Srivastava, V., Singh, S.S. & Biswas, B. Manifold Preserving Features and Regression for Semantic Labelling in High Dimensional Images. Wireless Pers Commun 126, 3119–3146 (2022). https://doi.org/10.1007/s11277-022-09856-y

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