基于平均窗口平移的直方图相似性度量

A Novel Histogram Similarity Measure Using Average Translation of Bins

传统的直方图相似性评价方法如L1距离等,在直方图发生一定形变如伸缩、平移的情况下存在一定不足.为解决这一类问题,提出一种直方图平均窗口平移度量方法,通过统计2幅直方图相同窗口之间的取值差异及不同距离窗口之间的取值差异,求出以窗口距离为权值进行加权后的平均差异,将其作为评价2幅直方图相似性的标准.对不同自然图像的灰度直方图及受噪声、光照等影响下的图像的灰度直方图之间的相似性,用该评价方法和传统评价方法进行比较,表明该评价标准在直方图发生形变的情况下性能优于传统的度量标准,而在通常情况下则与传统评价标准具有一致性.采用该评价标准进行图像及目标搜索等应用的结果显示该方法有望被应用于图像检索、匹配及目标跟踪等领域.

Previous histogram similarity measures have disadvantages when several kinds of deformation happen to the histograms. A novel similarity measure is proposed based on average translation of histogram bins. The value differences of both corresponding and cross bins are calculated to get the average difference against bin distances, and the average translation of bins is used to measure the histogram similarity. Experiments on intensity histograms of images influenced by noise and illumination are carried out to compare the performance of different kinds of simi- larity measures. The results show that average translation of bins as a histogram similarity measure outperforms other measures under the condition of histogram deformation. It consists with the other measures under general condi- tions. Preliminary studies on the measure＇ s usefulness in image retrieval and object searching indicate its potential applications based on image histograms.