An Optimized Method for Image Classification Based on Bag of Words Model
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摘要: 该文应用词袋模型对图像进行分类,并针对传统词袋模型存在的不足进行改进,提出了一种基于兴趣区域(Region Of Interest, ROI)提取以及金字塔匹配原理的优化方法。首先对训练图像进行ROI提取,对得到的ROI区域进行密集尺度不变特征变换(Scale-Invariant Feature Transform, SIFT)特征的抽取和描述并生成视觉词典,由此产生的视觉词典更能精确的描述图像的特征,且能够抵抗多变的位置信息及背景信息的影响。其次应用金字塔匹配原理对图像进行基于视觉词典的直方图表示,代入支持向量机(Support Vector Machine, SVM)分类器进行分类。通过对Caltech 101和Caltech 256两个数据库进行实验,结果表明该方法较传统方法提高了分类的正确率,且能够在训练图像较少的情况下达到良好的分类效果。最后通过与现有同类方法的比较验证了该方法的优越性。Abstract: The Bag of Words (BoW) model is applied to object classification in this paper. An optimized method based on the combination of Region Of Interest (ROI) extraction and pyramid matching scheme is proposed to optimize and improve the traditional model in order to overcome the disadvantages. First, the ROI is extracted from training images and then the codebook is generated using the features which are extracted from the ROI instead of the entire images using dense Scale-Invariant Feature Transform (SIFT) descriptor. Therefore, the codebook can describe the features of the images more accurately and also can resist the impact of the various position information as well as background. Then the images will be represented as the histogram of codebook using pyramid matching scheme as the input of Support Vector Machine (SVM) classifier. The experiments are carried out based both Caltech 101 and Caltech 256 database. The results show that the proposed method performs better than the traditional method and the state of the art. What is more, the classification accuracy is good even though under lack of training images.
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