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Related Experiment Video

Updated: Apr 21, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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Weakly supervised visual dictionary learning by harnessing image attributes.

Yue Gao, Rongrong Ji, Wei Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 1, 2014
    PubMed
    Summary

    This study introduces a novel weakly supervised method for learning visual dictionaries using image attributes, bypassing the need for costly labels. This approach enhances bag-of-features (BoF) representations for computer vision tasks.

    Related Experiment Videos

    Last Updated: Apr 21, 2026

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.6K

    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Bag-of-features (BoFs) are crucial for computer vision, requiring effective dictionary learning to minimize quantization loss.
    • Current supervised dictionary learning methods are limited by the high cost of acquiring semantic image labels.

    Purpose of the Study:

    • To develop a weakly supervised dictionary learning approach that leverages image attributes, eliminating the need for explicit labels.
    • To improve the scalability and semantic preservation of bag-of-features representations.

    Main Methods:

    • A generative hidden Markov random field (HMRF) model is proposed, treating quantized codewords as observed states and image attributes as hidden states.
    • Dictionary learning is achieved through supervised grouping of observed states, guided by the HMRF's hidden states.

    Main Results:

    • The proposed method successfully incorporates image attributes for dictionary learning without requiring genuine supervision.
    • Experiments demonstrate superior performance compared to unsupervised dictionary learning approaches in large-scale image retrieval and classification.

    Conclusions:

    • Weakly supervised dictionary learning using image attributes offers a scalable and effective alternative to traditional methods.
    • The HMRF-based approach enables semantic-preserving BoF representations, advancing computer vision applications.