文章基本信息

标题：Automatic Image Annotation based on Dense Weighted Regional Graph
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作者：Masoumeh Boorjandi ; Zahra Rahmani Ghobadi ; Hassan Rashidi 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2017
卷号：8
期号：3
DOI：10.14569/IJACSA.2017.080346
出版社：Science and Information Society (SAI)
摘要：Automatic image annotation refers to create text labels in accordance with images' context automatically. Although, numerous studies have been conducted in this area for the past decade, existence of multiple labels and semantic gap between these labels and visual low-level features reduced its performance accuracy. In this paper, we suggested an annotation method, based on dense weighted regional graph. In this method, clustering areas was done by forming a dense regional graph of area classification based on strong fuzzy feature vector in images with great precision, as by weighting edges in the graph, less important areas are removed over time and thus semantic gap between low-level features of image and human interpretation of high-level concepts reduces much more. To evaluate the proposed method, COREL database, with 5,000 samples have been used. The results of the images in this database, show acceptable performance of the proposed method in comparison to other methods.
关键词：thesai; IJACSA Volume 8 Issue 3; automatic annotation; dense weighted regional graph; segmentation; feature vector