图象分类系统的建立是信息检索以及模式识别中一个重要部分，其中，特征选择问题，即确定描述图象的特征参数是需要解决的关键问题，基于和图象检索技术的研究，近来得到了广泛的关注，由图象特征向量维数过高而引起的图象检索困难是基于内容的图象检索技术研究所面临的一个挑战，因此需要寻找一个有效降维技术，为解决此问题，设计了一个新的图象分类标准模型，通过寻找不同的特征组合来作为分类标准，进而提出了一种算法，用于实现此模型，实验结果显示，该模型能实现图象特征向量降维，并且算法能够极大地降低计算所花费的时间，同时，多种不同分类标准的引入，使得本方法能与信息检索技术进行有效的结合，为个性化信息检索提供一种实现思路。

Image classification system is an important part of any information retrieval system and pattern recognition system, and its key issue is to select some appropriate feature bindings of an image. Recent years content based image retrieval has been a very active research area. The dimension of the image feature vectors is normally very high and it's hard to index images. One of the main challenges in content based image retrieval is to develop techniques of performing dimension reduction. In this paper, a new model of searching multiple classification criterions has been proposed in which different feature bindings were formed to find new classification criterions, and a new algorithm was designed for this model. The experimental results shown that the proposed model can perform dimension reduction. The algorithm for the model is capable to reduce computational time which was also illustrated with results. The multiple criterions in combination with the information retrieval techniques can implement personalized information retrieval, and some results were given in last section.