显著对象的非监督粗糙认知算法

提出了一种显著对象非监督粗糙认知算法.算法首先定义了一种粒计算模型，然后按双概念拓扑划分论域，依据尺度过滤掉过小拓扑等价类；用拓扑连通强度、拓扑分布密度等计算出拓扑等价类的拓扑显著度；借改进Fisher线性判别算法找到最大跃变点，裁掉拓扑显著度过小的拓扑等价类，得到候选区；以维扫梯度等捕捉拓扑等价类间的渐变模式，完成局部粗糙分割，得到候选对象，更新候选对象的拓扑显著度；再次调用Fisher线性判别算法裁减，如果还剩多个对象，用位权选择最终显著对象.最后，以实验分步验证了算法的执行过程，并与同类3种算法的提取结果作了比较分析，证实了新算法有着较优的语义逼近能力和快捷的速度.

An Unsupervised Rough Cognition Algorithm for Salient Object Extraction

An unsupervised rough cognition algorithm based on granular computing for salient object extraction is proposed in this paper.Firstly,a granular computing model,as a way to simulate human thinking,is defined.Then,the following steps are done successively:1)Partition the universe according to two concepts respectively,and filter topology equivalence classes with smaller scale;2)Quantize the significance of topology equivalence classes with topology connectivity and topology distribution density;3)Find the cut-off position in the significance sequence with the improved Fisher’s linear discriminant algorithm,and then obtain the candidate regions by removing non-significant topology equivalence classes;4)Express the gradual changing pattern with dimensional scan gradient,which is used to do the local rough segmentation until candidate objects are available;merge the candidate objects that follow the gradual changing pattern and refresh the significant values of these candidate objects;5)Run the Fisher’s linear discriminant algorithm again,and determine the final salient object according to position weight if more than one candidate object is left.Finally,the executing process of the proposed approach is validated by experiment step by step,and a comparative analysis with three recent methods is conducted,which shows the superiority of the proposed approach in terms of ability to approximate semantic of object and speed.