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Recognizing Multiple Objects via Regression Incorporating the Co-occurrence of Categories

机译：通过结合类别共现的回归识别多个对象

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摘要

Most previous methods for generic object recognition explicitly or implicitly assume that an image contains objects from a single category, although objects from multiple categories often appear together in an image. In this paper, we present a novel method for object recognition that explicitly deals with objects of multiple categories coexisting in an image. Furthermore, our proposed method aims to recognize objects by taking advantage of a scene's context represented by the co-occurrence relationship between object categories. Specifically, our method estimates the mixture ratios of multiple categories in an image via MAP regression, where the likelihood is computed based on the linear combination model of frequency distributions of local features, and the prior probability is computed from the co-occurrence relation. We conducted a number of experiments using the PASCAL dataset, and obtained the results that lend support to the effectiveness of the proposed method.
机译：以前的大多数通用对象识别方法都显式或隐式地假定图像包含来自单个类别的对象，尽管来自多个类别的对象通常一起出现在图像中。在本文中，我们提出了一种新颖的对象识别方法，该方法可显式处理图像中共存的多个类别的对象。此外，我们提出的方法旨在通过利用由对象类别之间的共现关系表示的场景上下文来识别对象。具体来说，我们的方法通过MAP回归估算图像中多个类别的混合比，其中似然率是根据局部特征的频率分布的线性组合模型计算的，而先验概率是根据共现关系计算的。我们使用PASCAL数据集进行了许多实验，并获得了支持该方法有效性的结果。

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  • 来源
  • 会议地点 Tokyo(JP);Tokyo(JP)
  • 作者

    Takahiro Okabe; Yuhi Kondo; Kris M. Kitani; Yoichi Sato;

  • 作者单位

    Institute of Industrial Science, The University of Tokyo;

    Institute of Industrial Science, The University of Tokyo Sony Corporation;

    Institute of Industrial Science, The University of Tokyo Graduate School of Information Systems, The University of Electro-Communications;

    Institute of Industrial Science, The University of Tokyo;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机网络;
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