【作者】 朱涛；

【导师】 杨苏；

【作者基本信息】 哈尔滨工业大学 ， 计算机科学与技术， 2009， 硕士

【摘要】 局部泛化误差模型是一种评估分类器泛化能力的新型误差模型,以往关于该误差模型的主要研究都集中在分类器的结构选择和特征选择上,本文是局部泛化误差模型在样本选择领域的初步尝试,在此基础上提出了基于局部泛化误差模型的协同学习算法,并将其应用于基于内容图像检索中。在基于内容的图像检索中,已标注的训练样本往往比较有限,而大量存在的是未标注的样本,如何利用这些未标注的样本提高图像检索的精度,是近年来的研究热点。与完全依赖训练集的全监督学习方法不同,半监督学习方法可以有效地利用未标注样本。其中,协同学习通过两个条件独立又具有充分分类能力的特征子集构造出两个不同的分类器,训练好的每个分类器通过挑选出信任度最高的未标注样本并赋予其最可靠的类别标识,添加到另一个分类器的训练集合中。反复执行以上操作直到结果满意为止。通过这种方法,可以有效地扩充训练集合,弥补训练样本不足的问题。在协同学习中,样本的选择至关重要,以往的研究都是以样本的后验概率(或近似)作为信任度的评估标准。本文提出了基于局部泛化误差的样本选择策略,以样本的局部泛化误差值作为信任度评估标准,在协同学习过程中选取出具有较小泛化误差值的未标注样本给另一分类器。本文分别用图像的颜色和纹理特征构造两个不同的径向基函数神经网络,结合协同学习思想实现基于内容的图像检索,通过大量实验表明基于局部泛化误差的样本选择方法比传统的信任度评估标准选择出的样本更加准确,检索精度得到了很大提高。图像检索中为有效减少用户相关反馈的次数,本文采用了基于局部泛化误差的主动学习方法,反馈一些具有较大局部泛化误差值的不确定信息样本让用户进行标注。实验表明,这种方法比随机反馈的方法更加有效。更多还原

【Abstract】 Localized Generalization Error Model (L-GEM) is a new approach to estimate generalization error of a classifier. Prior researches have indicated that L-GEM out-performs current methods in neural network architecture selection and input feature selection. In this thesis, we apply the L-GEM to sample selection and proposed a semi-supervised learning method based on L-GEM. These proposed methods are adopted in a Content-Based Image Retrieval (CBIR).In CBIR, the number of unlabeled images is large while there are only a few labeled samples. Making a good use of these unlabeled samples is important to improve the retrieval precision. This has become a hot research topic in recent years. In contrast to fully labeled training dataset in supervised learning, semi-supervised learning method learns from both a small portion of labeled samples and a large portion of unlabeled samples. Co-training constructs two basic learners using two independent and sufficient sub-feature sets. Each trained basic learner classifies unlabeled samples and labels the most confident one which will be then added to the other learner’s training set. This process repeats until satisfactory result is achieved. In this way, the training set is expanded and the problem of limited training samples is releaved.In the co-training process, sample selection is a vital step. Traditional confidence criterion base on posterior probabilities or approximately directly. In this thesis, we proposed the L-GEM based sample selection criterion. In co-training process, we use sample’s L-GEM value as the confidence criterion and the confidence is inversely proportional to their L-GEM values. In our experiments, we use Radial Basis Function Neural Networks as basic learner and the results show that our proposed measurement is better than the traditional methods. The proposed method greatly improves image retrieval performance.Moreover, in order to reduce the number of relevance feedback iterations, we adopt active learning based on L-GEM. In detail, a few images with low confidence will be feeded-back to user for labeling. The experiment shows that it is more effective than random method.更多还原