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    • 1. 发明申请
    • LEARNED MID-LEVEL REPRESENTATION FOR CONTOUR AND OBJECT DETECTION
    • 用于轮廓和对象检测的学习中级表示
    • US20140270489A1
    • 2014-09-18
    • US13794857
    • 2013-03-12
    • MICROSOFT CORPORATION
    • Joseph Jaewhan LimPiotr DollarCharles Lawrence Zitnick, III
    • G06K9/62
    • G06K9/6253G06K9/4604
    • Various technologies described herein pertain to constructing mid-level sketch tokens for use in tasks, such as object detection and contour detection. Sketch patches can be extracted from binary images that comprise hand-drawn contours. The hand-drawn contours in the binary images can correspond to contours in training images. The sketch patches can be clustered to form sketch token classes. Moreover, color patches from the training images can be extracted and low-level features of the color patches can be computed. Further, a classifier that labels mid-level sketch tokens can be trained. Such training of the classifier can be through supervised learning of a mapping from the low-level features of the color patches to the sketch token classes.
    • 本文描述的各种技术涉及构建用于任务的中级草图令牌,例如对象检测和轮廓检测。 草图补丁可以从包含手绘轮廓的二进制图像中提取出来。 二进制图像中的手绘轮廓可以对应于训练图像中的轮廓。 草图修补程序可以聚类以形成草图标记类。 此外,可以提取来自训练图像的色块,并且可以计算出色块的低级特征。 此外,可以训练标记中级草图标记的分类器。 分类器的这种训练可以通过从色标的低级特征到草图标记类的映射的监督学习。