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Correlated Topic Vector for Scene Classification.

Pengxu Wei, Fei Qin, Fang Wan

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 20, 2017
    PubMed
    Summary
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    This study introduces a new generative image representation called correlated topic vector to capture semantic relationships in scene images. This method enhances recognition accuracy by utilizing topic correlations, outperforming existing techniques.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Scene images contain inherent semantic correlations, especially in large datasets.
    • Conventional feature encoding methods like Fisher vector often overlook these topic correlations.
    • Existing models may lack discriminative power due to the absence of correlation modeling.

    Purpose of the Study:

    • To propose a novel generative image representation, the correlated topic vector, for modeling semantic correlations in scene images.
    • To leverage topic correlations to enhance the discriminative capability of generative models for improved image recognition.
    • To integrate the benefits of Fisher vector with topic correlation modeling.

    Main Methods:

    • Developed the correlated topic vector based on the correlated topic model to utilize topic correlations.

    Related Experiment Videos

  • Incorporated the Fisher kernel method to leverage visual word contributions to topics.
  • Combined correlated topic vector with deep convolutional neural network (CNN) features and a Gibbs sampling solution.

    • Main Results:

    • The correlated topic vector significantly improves deep CNN features for scene image analysis.
    • Experimental results show superior performance compared to existing Fisher kernel-based features.
    • Demonstrated the effectiveness on two large-scale scene image datasets.

    Conclusions:

    • The correlated topic vector offers a powerful approach for modeling semantic correlations in scene images.
    • This novel representation enhances image recognition accuracy, particularly for complex and large-scale datasets.
    • The method shows significant potential for advancing scene image understanding and analysis.