EXTENT: fusing context, content, and semantic ontology for photo annotation

E. Chang
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引用次数: 15

Abstract

This architecture paper presents EXTENT, a probabilistic framework that uses influence diagrams to fuse metadata of multiple modalities for photo annotation. EXTENT fuses contextual information (location, time, and camera parameters), photo content (perceptual features), and semantic ontology in a synergistic way. It uses causal strengths to encode causalities between variables, and between variables and semantic labels. Through a landmark-recognition case study, we show that EXTENT can provide high-quality annotation, substantially better than any traditional unimodal methods.
EXTENT:融合上下文、内容和语义本体进行照片注释
本架构论文介绍了 EXTENT,这是一个使用影响图融合多种模式元数据进行照片标注的概率框架。EXTENT 以协同方式融合了上下文信息(位置、时间和相机参数)、照片内容(感知特征)和语义本体。它使用因果强度来编码变量之间以及变量与语义标签之间的因果关系。通过地标识别案例研究,我们表明 EXTENT 可以提供高质量的注释,大大优于任何传统的单模态方法。
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