Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

Research on the Contextual Information in Scene Classification

  • @INPROCEEDINGS{10.1007/978-3-030-00557-3_34,
        author={Pan Feng and Danyang Qin and Ping Ji and Jingya Ma},
        title={Research on the Contextual Information in Scene Classification},
        proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
        proceedings_a={MLICOM},
        year={2018},
        month={10},
        keywords={Contextual information Semantic localization Scene classification},
        doi={10.1007/978-3-030-00557-3_34}
    }
    
  • Pan Feng
    Danyang Qin
    Ping Ji
    Jingya Ma
    Year: 2018
    Research on the Contextual Information in Scene Classification
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-00557-3_34
Pan Feng1, Danyang Qin1,*, Ping Ji1, Jingya Ma1
  • 1: Heilongjiang University
*Contact email: qindanyang@hlju.edu.cn

Abstract

The classical localization approaches only focus on the performance of features extracted from images but ignore contextual information hidden in the images. In this paper, it is annotated on the images and SVM model is used to classify different images for semantic localization. Supervised Latent Dirichlet Allocation (sLDA) model is introduced to obtain the annotations, and the standard SIFT algorithm is improved to extract feature descriptors. Two situations are designed for the acquisition of contextual annotations, which are to provide the accurate contextual annotations directly and to infer contextual information by sLDA model. The effect of contextual information in scene classification is simulated and verified.