• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Feature hashing for fast image retrieval
【24h】

Feature hashing for fast image retrieval

机译：特征哈希用于快速图像检索

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

摘要

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very time-consuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.
机译：当前，基于内容的图像检索的研究主要集中在鲁棒的特征提取上。但是，由于在线图像的指数增长，因此有必要考虑在大型图像中进行搜索，这非常耗时且不可缩放。因此，我们需要非常注意图像检索的效率。在本文中，我们提出了一种用于图像检索的特征散列方法，该方法不仅生成用于图像表示的紧凑指纹，而且还防止了散列过程中的巨大语义损失。为了生成指纹，构造并最小化了语义损失的目标函数，该函数结合了特征数据的邻域结构和映射错误的影响。由于基于机器学习的散列有效地保留了数据的邻域结构，因此它产生具有强可辨性的视觉单词。此外，所生成的二进制代码使图像表示构建具有较低的复杂性，从而使其高效且可扩展到大型数据库。实验结果表明我们的方法具有良好的性能。

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