利用熵和分形压缩编码的图像检索新方法

图像的抽象描述和特征提取是基于内容的图像检索系统中需要解决的关键问题之一，文章提出了一种图像信息熵和分形编码相结合的图像检索方法．首先，计算图像库中的各幅图像的信息熵，比较设定的阈值对图像库进行预分类；其次．利用Jacquin方法计算得到查询图像的分形IFS编码，应用到图像库同类的图像进行分形迭代解码；最后，计算解码图像与查询图像的相似距离得到检索结果．实验结果表明，与直接像素值相似匹配方法相比，该法在基本保证图像检索效率的前提下，极大地提高了检索时间．

Image Retrieval Method Using Entropy and Fractal Image Coding

Describing and extracting image＇s feature is a key question in content-based image retrieval system, this paper puts forward a new image retrieval method using image information entropy and fractal IFS coding. First, each of image in the database is classified by computing information entropy which is compared with a given threshold. Second, the query image＇s fractal coding is obtained with Jacquin method, which is applied to the same kind of database images with fractal iteration decoding. Finally,the image retrieval result is obtained by matching the similar distance of the query image and the iterated decoding image. Experimental results show that, compared with the direet image pixels similar matching method, our scheme improves the retrieval time greatly and guarantees the retrieval accuracy, thus our proposed method is effective and feasible.