Error recovered hierarchical classification

Shiai Zhu, Xiao-Yong Wei, C. Ngo
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引用次数: 7

Abstract

Hierarchical classification (HC) is a popular and efficient way for detecting the semantic concepts from the images. However, the conventional HC, which always selects the branch with the highest classification response to go on, has the risk of propagating serious errors from higher levels of the hierarchy to the lower levels. We argue that the highest-response-first strategy is too arbitrary, because the candidate nodes are considered individually which ignores the semantic relationship among them. In this paper, we propose a novel method for HC, which is able to utilize the semantic relationship among candidate nodes and their children to recover the responses of unreliable classifiers of the candidate nodes, with the hope of providing the branch selection a more globally valid and semantically consistent view. The experimental results show that the proposed method outperforms the conventional HC methods and achieves a satisfactory balance between the accuracy and efficiency.
错误恢复层次分类
层次分类(HC)是从图像中检测语义概念的一种流行且有效的方法。然而,传统的HC总是选择具有最高分类响应的分支继续进行,这有将严重错误从层次结构的较高级别传播到较低级别的风险。我们认为,最高响应优先策略过于武断,因为候选节点被单独考虑,忽略了它们之间的语义关系。在本文中,我们提出了一种新的HC方法,该方法能够利用候选节点及其子节点之间的语义关系来恢复候选节点的不可靠分类器的响应,以期为分支选择提供一个更全局有效和语义一致的视图。实验结果表明,该方法优于传统的HC方法,在精度和效率之间取得了令人满意的平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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