一种基于改进的支持向量机多分类器图像标注方法

针对多标签图像标注问题,提出一种改进的支持向量机多分类器图像标注方法。首先引入直方图交叉距离作为核函数,然后把传统支持向量机的输出值变换为样本到超平面的距离。基于这两点改进,采用一种特征选择方法,从众多的图像特征中,选择那些相互之间冗余度较小的视觉特征,分别建立分类器,最终形成以距离大小为判别依据的支持向量机多分类器模型。此外,在建立分类器时,考虑到训练图像中不同标签类样本分布的不均匀,引入了一个关于图像类标签的概率分布值做为分类器的权重系数。实验采用ImageCLEF提供的图像标注数据集,在其上的实验验证了所采用的特征选择算法和多分类模型的有效性,其标注精度要优于其他传统分类模型,并且,实验结果与最新的方法相比也具有一定的竞争力。

Improved SVM multiple classifiers for image annotation

We propose a novel classifier for the multi label image annotation task based on an improved SVM. We firstly define a histogram intersection distance for the SVM kernel function. Then, the original SVM output result is transformed to the distance between a given sample and the hyperplane. Additionally, a feature selection method is developed for our model, and we choose those visual features with small correlations between them to establish the SVM based classifier. Furthermore, on account of the uneven distribution of different image categories, we also introduce a probability weighted strategy in our SVM model. Experiments on ImageCLEF dataset not only confirm the effectiveness of the proposed model, but also show that the proposed feature selection method is very suitable for the classifier. Compared with the traditional classifiers, our method obtains the optimal results, and is competitive to the state-of-the-art methods.