• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国政府科技报告 >Distributed Kernelized Locality-Sensitive Hashing for Faster Image Based Navigation.
【24h】

Distributed Kernelized Locality-Sensitive Hashing for Faster Image Based Navigation.

机译：基于分布式核心局部敏感哈希的快速图像导航。

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Content based image retrieval (CBIR) remains one of the most heavily researched areas in computer vision. Different image retrieval techniques and algorithms have been implemented and used in localization research, object recognition applications, and commercially by companies such as Facebook, Google, and Yahoo!. Current methods for image retrieval become problematic when implemented on image datasets that can easily reach billions of images. In order to process extremely large datasets, the computation must be distributed across a cluster of machines using software such as Apache Hadoop. There are many different algorithms for conducting content based image retrieval, but this research focuses on Kernelized Locality-Sensitive Hashing (KLSH). For the first time, a distributed implementation of the KLSH algorithm using the MapReduce programming paradigm performs CBIR and localization using an urban environment image dataset. This new distributed algorithm is shown to be 4.8 times faster than a brute force linear search while still maintaining localization accuracy within 8.5 meters.

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号