An Efficient Image Retrieval Model Using Fuzzy Semantic Concepts

Tsun-Wei Chang, Yo-Ping Huang, F. Sandnes
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Abstract

Concepts can add knowledge to the interpretation of image contents. However, mapping low-level features to high-level image semantics is still an ongoing challenge for researchers. In this paper an integrated model of fuzzy centrality and intensity concepts, together with the concept hierarchy is proposed to efficiently retrieving images. The self-organization feature map is applied to construct a three-layer concept hierarchy for image archives. Thus, search for the image concepts can be effectively achieved by detecting the presences of the relevant bottom-level image primitive features. In other words, an image can be categorized into multiple semantics. Consequently, the retrieval accuracy can be improved by searching the multiple categories. The methodology of the proposed model will be illustrated in this paper and the experimental results will be presented to demonstrate the efficiency in retrieving images.
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基于模糊语义概念的高效图像检索模型
概念可以为图像内容的解释增加知识。然而,将低级特征映射到高级图像语义仍然是研究人员面临的一个挑战。本文提出了一种模糊中心性和强度概念的集成模型,并结合概念层次来实现图像的高效检索。应用自组织特征映射构建了图像档案的三层概念层次结构。因此,通过检测相关底层图像原语特征的存在,可以有效地实现对图像概念的搜索。换句话说,图像可以分为多个语义。因此,通过对多个分类进行搜索,可以提高检索精度。本文将说明所提出的模型的方法,并给出实验结果以证明检索图像的效率。
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