Enhancing sports image search and retrieval using multi-modality ontology

Yomna Hatem, R. Ismail, S. Rady, K. Bahnasy
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引用次数: 0

Abstract

Extracting knowledge from multimedia contents represents recently a big challenge. Organizing and analyzing multimedia collections requires specific tools for extracting knowledge from the contents to enable effective and efficient filtering, searching and retrieval. The use of knowledge models, such as Ontology, is gaining interest among multimedia retrieval researches. This paper builds and integrates a multi-modality ontology to the conventional image annotation and retrieval methodology. The proposed knowledge-model integration highly improves the searching process. Two ontologies are proposed, domain and visual description ontologies. Experiments have demonstrated the efficiency of the proposed multi-modality ontology method when compared against the classical retrieval technique. The results show that using ontologies increases the performance to reach 1, 0.91 and 0.94 for Precision, Recall and F-measure respectively.
利用多模态本体增强运动图像检索
从多媒体内容中提取知识是最近的一大挑战。组织和分析多媒体集合需要特定的工具来从内容中提取知识,以实现有效和高效的过滤、搜索和检索。利用本体等知识模型是多媒体检索研究的热点。本文构建了一个多模态本体,并将其集成到传统的图像标注和检索方法中。所提出的知识模型集成大大改善了搜索过程。提出了两种本体:领域本体和视觉描述本体。实验结果表明,与传统检索技术相比,多模态本体方法具有较高的检索效率。结果表明,使用本体可以提高准确率、召回率和F-measure分别达到1.91、0.91和0.94。
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