颜色不变量的自适应聚类网络量化方法

给出了颜色不变量的自适应聚类网络量化算法。这种方法采用一组图象能自适应地影响量化矢量。把这种算法和均匀量化算法应用于CBIR系统中，并对它们的检索结果和时间复杂度进行比较，结果表明，该算法在检索的正确率时间的复杂度上均优于均匀量化方法。因而颜色不变量的自适应聚类网络量化方法是一种很好的矢量量化算法。

Adaptive Cluster Network Quantification for Color Invariants

We aim at finding a suitable quantification algorithm to encode color invariant for indexing and retrieving images. To this end, an adaptive cluster network quantification algorithm for color invariants is proposed. By using a set of training images to train the color invariant vector, this algorithm can acquire a suitable color invariants vector having an adaptive number of members. In this paper, we discuss in detail how the threshold and the step in the algorithm influence the number of the vector members. After having done many experiments, we get a vector of 29 members for our training images when the threshold is 1 0 and the step is 0 3. In this setting, the vector is rather robust. Then we also apply the algorithm and another bench algorithm, named averaging quantification algorithm, to a content based image retrieval system. Experiments have been conducted on a database consisting of 1 126 images taken from different image databases. In order to evaluate and compare the querying results, an application specific software is developed. In the view of the correctness of the querying results, comparing the adaptive quantification algorithm with the averaging quantification algorithm, we find the former is superior to the latter by 4%. In the view of the time complexity, although the former takes a long time to train quantification vector and to acquire a lookup table for the image database, it is much superior to the latter when retrieval proceeds. Finally a conclusion is obviously obtained that adaptive quantificaion algorithm is an excellent quantificaion algorithm for color invariants.