Implicit visual concept modeling in image / video annotation

K. Ntalianis, A. Doulamis, N. Tsapatsoulis
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引用次数: 2

Abstract

In this paper a novel approach for automatically annotating image databases is proposed. Despite most current approaches that are just based on spatial content analysis, the proposed method properly combines implicit feedback information and visual concept models for semantically annotating images. Our method can be easily adopted by any multimedia search engine, providing an intelligent way to even annotate completely non-annotated content. The proposed approach currently provides very interesting results in limited-content environments and it is expected to add significant value to billions of non-annotated images existing in the Web. Furthermore expert annotators can gain important knowledge relevant to user new trends, language idioms and styles of searching.
图像/视频标注中的隐式视觉概念建模
本文提出了一种新的图像数据库自动标注方法。尽管目前大多数方法都是基于空间内容分析,但该方法将隐式反馈信息与视觉概念模型相结合,实现了对图像的语义标注。我们的方法可以很容易地被任何多媒体搜索引擎采用,提供了一种智能的方式,甚至可以对完全没有注释的内容进行注释。所提出的方法目前在内容有限的环境中提供了非常有趣的结果,并且有望为Web上存在的数十亿未注释的图像增加重要的价值。此外,专家注释者可以获得与用户新趋势、语言习惯和搜索风格相关的重要知识。
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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