目标识别中的稳定图像特征组合发掘

The mining of stable image feature-compositions in object recognition

针对图像局部特征组合稳定性差和区分力不足的问题,通过对由图像半局部邻域特征挖掘得到的频繁项集进行统计学过滤、模式分解、模式总结及模式组成项间几何关系的建模,提出两种具有较强表征力和区分力的图像中层表示模型:类间共用稳定模式(inter-class common stable pattern)和类内特殊稳定模式(intra-class specialstable pattern)。在将这两种模式引入目标识别框架后,得到了相比同类方法较好的结果。

In order to improve the stability and discrimination of local feature combination for image representation, two image mediate-level representations, Inter-CSP（ inter-class common stable pattern） and ntra-SSP（ intra-class special stable pattern） are proposed. The details of processing are given, which can be divided into statistic-filtering, pattern decomposition, pattern summarization, and item-based geometric relation modeling on frequent item_sets mined from image semi-local features. A recognition framework is introduced based on Inter-CSP and Intra-SSP. The experiment results demonstrate that these two kinds of patterns are superior to classical methods.