当图像数量增长到一定数量后，基于浮点矢量形式表示的图像特征就不适合放置在内存中，欧氏距离的计算也将造成较大的时间开销。
For example, it costs more for storage and the distance computation is quite complex. With the number of images grows, it will be unsuitable for vector based image feature to stay in memory.
浮点矢量图像特征维数较高，且通常以欧氏距离作为矢量之间的相似度定义。
Float vector based image feature has high dimension and usually makes use of Euclidean distance as its similarity definition.
传统的以浮点矢量形式表示的图像特征，是基于内容的图像检索技术的基础。
The traditional float vector based image feature is the base of content based image retrieval techniques.
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