头肩图像检测在智能监视系统和基于内容的图像索引等方面都有广泛的应用，因而研究快速而且准确的头肩图像检测技术具有十分重要的意义。
It can be applied to Content-based Image Indexing and Smart Surveillance Systems. It is essential to develop robust and efficient algorithms to detect head and shoulders.
Google在2001年发布图像搜索功能时，只有2.5亿索引图像，不到10年，这个巨大的搜索功能已经可以检索超过100亿个图像了，每分钟有35小时的内容上传到YouTube。
When Google launched its image search feature in 2001, it had 250 million indexed images. Less than a decade later, the search giant has indexed over 10 billion images.
多维索引技术是基于内容检索的图像数据库的关键技术。
Multidimensional indexing technology is the key technology of content-based retrieval in image database.
在基于图像内容的图像检索系统中，搜索引擎检索图像类似于按照相似标准来查询图像。
In content based image retrieval system, search engine retrieves the images similar standard to the Cey words query image according to a similarity measure.
低层视觉特征提取、高维数据索引机制和相关反馈方法是面向大规模图像库基于内容检索的三个关键问题。
Visional feature extraction, high dimensional indexing mechanism and relevance feedback are three important issues in content-based image retrieval.
图像视频中的文字为描述和注释图像内容提供了十分重要的信息，也是图像视频索引与检索的重要依据。
Text in image and video plays an important role in describing and annotating image content, and also provides the important clues for video index and retrieval.
在降维的基础上，建立了一个新的索引机制，并以此加速大规模图像库的基于内容检索的进程。
Based on dimension reduction, it puts forward a new indexing structure to improve the performance of content-based retrieval of large image databases.
有效的高维索引机制是基于内容的图像检索的关键技术，具有重要的理论意义和应用价值。
Efficient indexing schemes for high-dimensional data are important for Content-Based Image Retrieval, with theoretical and applicable value as result.
有效的高维索引机制是基于内容的图像检索的关键技术，具有重要的理论意义和应用价值。
Efficient indexing schemes for high-dimensional data are important for Content-Based Image Retrieval, with theoretical and applicable value as result.
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