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    • 7. 发明授权
    • Robust large-scale visual codebook construction
    • 坚固的大型视觉代码簿建设
    • US08422802B2
    • 2013-04-16
    • US13077735
    • 2011-03-31
    • Linjun YangDarui LiXian-Sheng HuaHong-Jiang Zhang
    • Linjun YangDarui LiXian-Sheng HuaHong-Jiang Zhang
    • G06K9/36
    • G06K9/6223
    • Techniques for construction of a visual codebook are described herein. Feature points may be extracted from large numbers of images. In one example, images providing N feature points may be used to construct a codebook of K words. The centers of each of K clusters of feature points may be initialized. In a looping or iterative manner, an assignment step assigns each feature point to a cluster and an update step locates a center of each cluster. The feature points may be assigned to a cluster based on a lesser of a distance to a center of a previously assigned cluster and a distance to a center derived by operation of an approximate nearest neighbor algorithm having aspects of randomization. The loop terminates when the feature points have sufficiently converged to their respective clusters. Centers of the clusters represent visual words, which may be used to construct the visual codebook.
    • 本文描述了构建视觉码本的技术。 特征点可以从大量图像中提取出来。 在一个示例中,提供N个特征点的图像可以用于构造K个字的码本。 可以初始化K个特征点中的每一个的中心。 以循环或迭代的方式,分配步骤将每个特征点分配给集群,并且更新步骤定位每个集群的中心。 可以基于距先前分配的簇的中心的距离中较小的一个特征点来分配特征点,以及通过具有随机化方面的近似最近邻算法的操作导出的到中心的距离。 当特征点已经充分收敛到它们各自的簇时,环路终止。 集群的中心表示视觉词,可用于构建视觉码本。
    • 8. 发明授权
    • Kernelized spatial-contextual image classification
    • 内核空间上下文图像分类
    • US08131086B2
    • 2012-03-06
    • US12237298
    • 2008-09-24
    • Xian-Sheng HuaGuo-Jun QiYong RuiHong-Jiang Zhang
    • Xian-Sheng HuaGuo-Jun QiYong RuiHong-Jiang Zhang
    • G06K9/68
    • G06K9/469G06K9/6297
    • Kernelized spatial-contextual image classification is disclosed. One embodiment comprises generating a first spatial-contextual model to represent a first image, the first spatial-contextual model having a plurality of interconnected nodes arranged in a first pattern of connections with each node connected to at least one other node, generating a second spatial-contextual model to represent a second image using the first pattern of connections, and estimating the distance between corresponding nodes in the first spatial-contextual model and the second spatial-contextual model based on a relationship with adjacent connected nodes to determine a distance between the first image and the second image.
    • 公开了内核空间上下文图像分类。 一个实施例包括生成第一空间上下文模型以表示第一图像,第一空间上下文模型具有以与连接到至少一个其他节点的每个节点连接的第一连接方式布置的多个互连节点,产生第二空间 - 使用所述第一连接模式来表示第二图像,以及基于与相邻连接节点的关系来估计所述第一空间 - 上下文模型中的对应节点与所述第二空间 - 上下文模型之间的距离,以确定所述第二图像之间的距离 第一个图像和第二个图像。