textual retrieval

The paper describes the problem of textual case based retrieval with quotient space theory, and divides the processes of textual case base retrieval in to dividing the quotient space for user text case and searching.

本文用粒度计算的商空间理论描述文本案例检索问题，将文本案例检索分为为用户文本案例划分商空间和文本案例检索两个过程。

Efficient technology of image retrieval can greatly help people obtain entertainment on Internet and enhance the quality of lives. Nowadays, textual retrieval performs well.

高效的图像检索技术能够极大地有助于人们在互联网上进行数字娱乐，提高人们的生活品质。

The main purpose that introduces the granularity thinking of human beings in the textual case retrieval is to reduce the computational complexity, get better retrieval efficiency, and improve the retrieval speed.

本文引入人类这种粒度的思想，主要目标就是根据文本案例检索存在的问题，结合粒度计算的思想，以降低计算的复杂度，得到最佳的检索效率，并提高检索的速度。

Dividing the case base or cases into different granularity whit different size, to improve the speed and efficiency of dividing the quotient space for user textual case and the retrieval accuracy, and reduce the retrieval time-consuming.

将案例库划分为不同的粒度，以提高为用户文本案例划分商空间的速度和效率，同时提高检索的准确率，降低检索的时间耗费。通过实验验证，基于粒度的方法具有较好检索效果。

Content-based image retrieval ( CBIR) is a hotspot of image retrieval at computer filed. It is different from traditional textual retrieval.

基于内容的图像检索是目前计算机图像检索研究的热点，它不同于传统的文本检索，因此将其应用于医学图像领域具有重要意义。