基于空间PACT和颜色特征的场景语义识别

空间PACT是一种用来进行场景实例和类别识别的新型特征表示，它在PACT（Census变换直方图的主成分分析）的基础上结合最新的场景语义识别框架：空间金字塔，使之相比现存算法具有更高的识别率。针对场景语义识别的强度和效率，提出一种新型的识别方法，在空间PACT中引入潜在阶梯边缘模板，在几乎不影响识别率的基础上改进算法效率。同时通过引入颜色特征信息，获得具有更强语义识别能力的特征表示。实验结果表明，该算法具有计算效率高，识别率高，强语义识别的特点。

Scene semantic recognition based on spatial PACT and color feature

Spatial Principal component Analysis of Census Transform histograms (PACT) is a new representation for recognizing instances and categories of scenes.A spatial pyramid of PACT achieves higher accuracies than the current state-of-the-art methods in several place and scene.For the strength and efficiency of scene semantic recognition,a new scene semantic recognition method based on spatial PACT and color feature is proposed.By means of importing potential step edge template into spatial PACT,the efficiency of algorithm is improved greatly while the accuracy almost is not affected.At same time,by combining color feature,more powerful semantic representation is obtained.Experimental results show that the algorithm has high computational efficiency, high recognition .rate and powerful semantic recognition.