基于分层匹配追踪的快速图像检索

为从海量的图像资源中既准确又快速地检索出目 标图像,在传统的 图像检索模型中,图像的特征通常是从固定尺度的图像上提取出的,这将不可避免地降低整 个系统 实际应用能力。为解决这一问题,本文引入分层稀疏编码模型, 提出一种基于分层匹配追踪(HMP)的快速图像检索技术,实现多尺度情况下的图像检索。本 文方法从图像中提取的低层稀疏编码特征传递 到高层,并将提取的高维稀疏编码特征转换为改进后的PCAH特征,利用哈希特征的汉明距 离度量, 实现图像的快速检索。在公共数据集Caltech256和Corel5K上的实验 结果可以看出,本文方法的查 准率和查全率较其他哈希法分别提高了5%和10%以上,而且所用时间 也最短,表明本文方法不仅具有较高的准确率,还能保持较高的时间效率。

Fast image retrieval with hierarchical matching pursuit

In the traditional image retrieval model,the image future is usually extracted from a fixed scale image, which will inevitably degrade the performance of the whole system.M otivated by this,this paper introduces a hierarchical sparse coding model to realize image retrieval i n multi-scale cues,and proposes a fast image retrieval technology with hierarchical matching pursuit.I n this method,the low-layer sparse codes features extracted from the image are transferred to the high- layend then the extracted high dimensional sparse codes featues are transformed into the improved PCAH feature.After that,the Hamming distance is used for fast image retrieval.It can be seen from the experiments o n two benchmark datasets of Caltech256and Corel5K that compared with other hash methods,our method improv es the precision and recall rate by 5% and 10% respectively,with the least running time.Experimental results demonstrate that the proposed method not only obtains better accuracy,but also has higher time effic iency.