基于核密度估计和分形编码算法的图像检索技术研究

Research on Image Retrieval Based on Kernel Density Estimation and Fractal Coding Algorithm

为了提高基于分形压缩编码图像检索技术的应用价值,针对自然图像提出了一种联合参数的图像检索方法,从分形编码中提取鲁棒性(旋转、平移、缩放等不变性)索引,即从由值域块均值构成的解码近似图像中提取改进的Hu不变矩特征量作为检索索引,再与分形编码参数的核密度估计统计特征相结合,其中,核密度估计方法中采用可变带宽。然后采用2个索引的加权和来比较图像的相似度。实验结果表明,使用2个索引的加权和比使用单独索引具有更好的检索结果。

To improve the application value of image retrieval technology based on image fractal coding, aiming at natural images, a robust index （rotation, translation, scaling invariance） extracted from fractal parameters is proposed and that is improved Hu invariant moment. The index is extracted from an approximate image constructed by mean range blocks. Then combines statistic characteristic of fractal parameters with the Hu invariant moment index, the weighed indices are employed to compare the similarities among images. The experimental results show that the weighted indices perform better than a separate index.