Object detection in aerial imagery has been well studied in computer vision for years. However, given the complexity of large variations of the appearance of the object and the background in a typical aerial image, a robust and efficient detection is still considered as an open and challenging problem. In this paper, we present the enhanced semi-supervised learning (ESL) framework and apply this framework to revising an object detection methodology we have developed in a previous effort. Theoretic analysis and experimental evaluation using the UCI machine learning repository clearly indicate the superiority of the ESL framework. The performance evaluations of the revised object detection methodology against the original one clearly demonstrate the promise and superiority of this approach.


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    Title :

    Object detection in aerial imagery based on enhanced semi-supervised learning


    Contributors:
    Jian Yao, (author) / Zhongfei Zhang, (author)


    Publication date :

    2005-01-01


    Size :

    171482 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Object Detection in Aerial Imagery Based on Enhanced Semi-Supervised Learning

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