首页> 外文会议>Information Retrieval Technology; Lecture Notes in Computer Science; 4182 >Incorporating Prior Knowledge into Multi-label Boosting for Cross-Modal Image Annotation and Retrieval
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Incorporating Prior Knowledge into Multi-label Boosting for Cross-Modal Image Annotation and Retrieval

机译:将先验知识整合到用于跨模态图像注释和检索的多标签增强中

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Automatic image annotation (AIA) has proved to be an effective and promising solution to automatically deduce the high-level semantics from low-level visual features. In this paper, we formulate the task of image annotation as a multi-label, multi class semantic image classification problem and propose a simple yet effective joint classification framework in which probabilistic multi label boosting and contextual semantic constraints are integrated seamlessly. We conducted experiments on a medium-sized image collection including about 5000 images from Corel Stock Photo CDs. The experimental results demonstrated that the annotation performance of our proposed method is comparable to state-of-the-art approaches, showing the effectiveness and feasibility of the proposed unified framework.
机译:事实证明,自动图像注释(AIA)是一种有效且有前途的解决方案,可以从低级视觉特征自动推断出高级语义。在本文中,我们将图像标注的任务表述为一个多标签,多类别的语义图像分类问题,并提出了一个简单而有效的联合分类框架,该框架将概率多标签增强和上下文语义约束无缝集成。我们对中等大小的图像集进行了实验,其中包括来自Corel Stock Photo CD的大约5000张图像。实验结果表明,我们提出的方法的注释性能可与最新技术相媲美,显示了提出的统一框架的有效性和可行性。

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