A framework for image classification

Mamoun Awad, Lei Wang, Yuhan Chin, Latifur Khan, George Chen, Fehmi Chebil

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    Image annotation process requires time and human intervention. In this research we propose a framework to incrementally annotate images in the database based on user feedback. At the beginning users provide some annotations for images manually as a ground truth. Classifier will be trained based on this ground truth. The classifier predicts annotation for new images that are not part of the ground truth. Feedback is collected from the users to increase the size of the training set and then the classifier is retrained. The system strives to capture feedback from users and retrains the classifier on the new training set. Our proposed framework facilitates semi-automatic image annotation

    Original languageEnglish
    Title of host publication7th IEEE Southwest Symposium on Image Analysis and Interpretation
    Pages134-138
    Number of pages5
    Publication statusPublished - 2006
    Event7th IEEE Southwest Symposium on Image Analysis and Interpretation - Denver, CO, United States
    Duration: Mar 26 2006Mar 28 2006

    Publication series

    NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
    Volume2006

    Other

    Other7th IEEE Southwest Symposium on Image Analysis and Interpretation
    Country/TerritoryUnited States
    CityDenver, CO
    Period3/26/063/28/06

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition
    • Computer Science Applications

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