语义指向性特征聚类的图像检索算法研究

Investigation on Image Retrieval Algorithm with Semantic Directed Feature Clustering

在大型多媒体数据库中,需要进行图像检索实现感兴趣图像的准确索引和多媒体数据库的准确访问。传统方法采用关联信息人工标注方法进行图像检索,随着数据库中图像信息的增大,标注检索效率较低。为提高大型多媒体数据库中图像检索的效率和精度,提出了一种基于语义指向性特征聚类的图像检索算法。该算法通过图像向量量化编码实现图像压缩,对图像中的文本信息点进行频域特征点归类,对出现重叠文本的图像帧序列进行向量量化分解,提取梯度差异信息特征,实现语义指向性特征聚类,将窗口中梯度最大值进行自适应加权,提取量化编码压缩图像的语义特征信息,采用模糊C均值聚类算法对提取的语义特征进行分类标注,由此实现大型多媒体数据库中图像的准确检索和调度。仿真结果表明,该算法的图像检索准确度较高,图像帧差为零,输出图像的峰值信噪比优于传统方法,展示了较好的图像检索能力。

In large multimedia database,it is necessary to carry out the image retrieval to realize the accurate index of the interested image and correct access of the multimedia database. Traditional methods take the manually labeling method of association information for image retrieval, and with the increase of the image information in database, the efficiency is low. In order to improve the efficiency and precision of image retrieval in large multimedia database, an image retrieval algorithm is proposed based on semantic directed feature clustering. The algorithm uses vector quantization for image compression, and the frequency domain feature point of text information in the image are classified. The text image frame sequence is taken with vector quantization decomposition, extraction of feature of gradient difference in- formation, realization of the feature clustering of semantic orientation, adaptive weighting of the maximum gradient in the window. Se- mantic feature information of the quantization coding compression image is extracted, and fuzzy C-means clustering algorithm is used to extract semantic features labeling, realizing the exact matching and scheduling of large multimedia database. Simulation results show that the accuracy of the algorithm for image retrieval is improved, and image frame difference is zero, and output image PSNR is more than traditional methods, which has good image retrieval performance.