基于局部颜色-空间特征的图像语义概念检测

Image Semantic Concept Detection Based on Local Color-spatial Feature

针对基于语义的图像检索系统,提出了一种基于局部颜色-空间特征的图像语义概念检测方法。各种基于颜色、纹理和形状的全局特征都存在着众多信息冗余项和干扰项,而该文提出的局部颜色-空间特征则是利用语义概念层的先验知识进行特征降维后提取出的特征,它能更好地描述图像的语义内容,且具有容易提取、计算复杂度低的优点。实验结果表明,基于局部颜色-空间特征的概念检测方法优于基于全局特征的概念检测方法,将其用于图像检索后的检索精度比采用基于全局颜色特征的方法提高了36.4%。

In this paper, a novel approach to semantic concept detection based on local color-spatial feature is proposed. There is much noise and redundant information in many global features of color, texture and shape. This local color-spatial feature contains more semantic contents of image than other global features by using prior knowledge of semantic concept level to reduce feature dimensions. Experiment results are reported and presented to demonstrate the effectiveness and efficiency of the proposed approach. Average precision of image retrieval by using the semantic concept detection method based on local color-spatial feature is 36.4% higher than the method based on global color feature.