基于相容粗糙集的图形图像信息预检索

Graphics and Image Pre-indexing based on Tolerance Rough Sets

早期的利用粗糙集理论进行信息检索都是以“等价粗糙集模型”为基础的 ,但是等价粗糙集的性质限制了该方法的应用范围 .为此有些研究者提出以“相容粗糙集模型”代替“等价粗糙集模型”的新的信息检索的概念 .此概念的关键在于“关键词的同时发生”和关于相容粗包含的“匹配算法”.提出了一种利用“相容粗糙集”的理论对图形图像进行预检索的新方法 ,即在相容类的近似空间里对图形图像进行预检索 .为了验证这种新方法的有效性 ,在人脸图形和图像库中做了若干实验 ,实验结果表明 ,该方法可以有效地克服等价粗糙集在图形图像检索方面的限制 。

All previous works on information retrieval using rough sets are based on equivalence rough sets model (ERSM), but the property of equivalence has limited its application fields. Some researchers have proposed a kind of tolerance rough sets model (TRSM) for information retrieval. The core of TRSM is tolerance classes constructed by the index co occurrence and a matching algorithm with tolerance rough inclusions. In this paper a new method for graphics and image pre indexing by tolerance rough sets is presented. This new approach organizes the graphics and image in an approximation space of tolerance classes. Experimental results obtained from human face graphics and image pre indexing show that the tolerance approach can effectively overcome the limitation of ERSM and enhance the efficiency of graphics and image indexing.