Image indexing & retrieval using intermediate features

Mohamad Obeid, B. Jedynak, M. Daoudi
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引用次数: 38

Abstract

Visual information retrieval systems use low-level features such as color, texture and shape for image queries. Users usually have a more abstract notion of what will satisfy them. Using low-level features to correspond to high-level abstractions is one aspect of the semantic gap.In this paper, we introduce intermediate features. These are low-level "semantic features" and "high level image" features. That is, in one hand, they can be arranged to produce high level concept and in another hand, they can be learned from a small annotated database. These features can then be used in an image retrieval system.We report experiments where intermediate features are textures. These are learned from a small annotated database. The resulting indexing procedure is then demonstrated to be superior to a standard color histrogram indexing.
使用中间特征的图像索引和检索
视觉信息检索系统使用颜色、纹理和形状等低级特征进行图像查询。用户通常有一个更抽象的概念,什么会满足他们。使用低级特征来对应高级抽象是语义缺口的一个方面。本文引入了中间特征。这些是低级的“语义特征”和“高级图像”特征。也就是说,一方面,它们可以被安排来产生高层次的概念,另一方面,它们可以从一个小的带注释的数据库中学习。这些特征可以在图像检索系统中使用。我们报告了中间特征是纹理的实验。这些是从一个小的带注释的数据库中学习到的。由此产生的索引程序,然后证明优于标准的颜色直方图索引。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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