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    Anomaly detection using local kernel density estimation and context-based regression

    Hu, W. and Gao, J. and Wu, O. and Du, J. and Maybank, Stephen J. (2018) Anomaly detection using local kernel density estimation and context-based regression. IEEE Transactions on Knowledge and Data Engineering 32 (2), pp. 218-233. ISSN 1041-4347.

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    Current local density-based anomaly detection methods are limited in that the local density estimation and the neighbourhood density estimation are not accurate enough for complex and large databases, and the detection performance depends on the size parameter of the neighborhood. In this paper, we propose a new kernel function to estimate samples' local densities and propose a weighted neighbourhood density estimation to increase the robustness to changes in the neighborhood size. We further propose a local kernel regression estimator and a hierarchical strategy for combining information from the multiple scale neighbourhoods to refine anomaly factors of samples. We apply our general anomaly detection method to image saliency detection by regarding salient pixels in objects as anomalies to the background regions. Local density estimation in the visual feature space and kernel-based saliency score propagation in the image enable the assignment of similar saliency values to homogeneous object regions. Experimental results on several benchmark datasets demonstrate that our anomaly detection methods overall outperform several state-of-the-art anomaly detection methods. The effectiveness of our image saliency detection method is validated by comparison with several state-of-the-art saliency detection methods.


    Item Type: Article
    Additional Information: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Stephen Maybank
    Date Deposited: 19 Nov 2018 09:28
    Last Modified: 09 Aug 2023 12:45


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