基于公共、社区和个人资源的丰富位置驱动标签云建议

D. Joshi, Jiebo Luo, Jie Yu, Phoury Lei, Andrew C. Gallagher
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引用次数: 5

摘要

最近的研究显示了地理标记在许多多媒体应用中的强大功能。在本文中,我们提出了一个集成和直观的系统,为地理标记的照片建议位置驱动的标签。从多个来源提取潜在标签,包括来自公共地理名称信息系统(GNIS)数据库的兴趣点(POI)标签,来自Flickr®图片的社区标签,以及通过用户自己,家人和朋友的照片集共享的个人标签。为了提高GNIS POI标签的有效性,首先检索地名标签袋,然后使用tf-idf和空间距离联合标准重新排序。根据与输入照片的距离和视觉相似性,在输入地理标记照片附近拍摄的照片中的社区标签进行排名。来自其他个人相关照片的个人标签天生就具有重要的权重,因为它们比一般的地名标签和社区标签具有更高的相关性,并且根据权重随着时间和距离的差异而衰减进行排名。最后,一组最相关的位置驱动标记以三个提到的源类别下的单个标记云的形式呈现给用户。标记云作为标记输入图像的直观建议。初步的用户评价显示了这三个类别各自的好处,并显示了集成标签建议系统的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rich location-driven tag cloud suggestions based on public, community, and personal sources
Recent research has shown the power of geotagging for many multimedia applications. In this paper, we present an integrated and intuitive system for suggesting location-driven tags for a geotagged photo. Potential tags from multiple sources are extracted, including points of interest (POI) tags from a public Geographic Names Information System (GNIS) database, community tags from Flickr® pictures, and personal tags shared through user's own, family and friends' photo collections. To increase the effectiveness of GNIS POI tags, bags of place name tags are first retrieved and then re-ranked using a combined tf-idf and spatial distance criteria. The community tags from photos taken in the vicinity of the input geotagged photo are ranked according to distance and visual similarity to the input photo. Personal tags from other personally related photos inherently carry a significant weight due to their high relevance than both the generic place name tags and community tags, and are ranked by weights decaying over time and distance differences. Finally, a rich set of the most relevant location-driven tags is presented to the user in the form of individual tags clouds under the three mentioned source categories. The tag clouds act as intuitive suggestions for tagging an input image. Preliminary user evaluation has revealed the respective benefits of the three categories and shown the effectiveness of the integrated tag suggestion system.
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