一种基于局部不变特征的图像特定场景检测方法

A method of specific image scene detection based on local invariant features

图像场景的自动检测,对于图像的标注以及语义检索具有非常重要的作用。本文研究根据实际应用的需要,围绕会晤、集会、海滩等八类特定场景图像的检测问题展开。首先对图像进行局部关键点的检测以及SIFT特征描述子的计算,从而提取图像的局部特征,在此基础上基于支撑向量机构建多分类器,进行特征训练,最终获得较为准确的检测结果。实验重点针对分类器核函数的确定以及特征选取策略等问题展开,实验结果表明,采用径向基核函数构建多分类器以及特征点按尺度大小排序取前n位的选取策略可以获得较为准确和鲁棒的特定场景检测结果。本方法在保证满足一定程度场景检测准确率的前提下,具有简单快速的特点,能够满足实际应用的需要。

Automatic image scene detection is very important to image annotation and semantic retrieval.According to the requirement of application,eight specific image scenes such as meeting,mass,beach,etc.were focused on.First,to extract the local features of images,the local key points were detected and reduced,and then the SIFT feature descriptors were calculated.Second,a multi-classifier based on support vector machine was constructed and the features for training were selected to achieve relatively accurate detection results.The experiments were designed to mainly focus on two problems,namely the decision of kernel function of classifier and the strategy of feature selection.Experimental results show that the method can achieve relatively accurate and robust results by using radial basis kernel function to construct classifier and the feature extraction strategy of selecting the top n key points by the scale size order.This method is simple and fast,and can satisfy the actual requirements of application for relatively high precision.