【作者】 王红霞；

【导师】 杨克俭；

【作者基本信息】 武汉理工大学 ， 计算机应用技术， 2012， 博士

【摘要】 视觉物体分类是图像检索、海量视频数据应用、视觉感知替代、自动机器人和交互式游戏等应用中极具挑战性的关键技术,具有广泛的应用需求和重大的应用研究价值。视觉物体分类的研究目标是发现图像中的物体并决定其所属类别。本文在视觉词袋模型框架下,研究视觉物体分类中的局部特征建模方法,包括局部特征线性编码、池化方式以及空间匹配方案,并给出了相应的改进方法,主要工作及创新点如下：(1)提出了局部约束主成分线性编码方法。从分析局部特征与K个近邻视觉单词的线性相关性和检验局部线性编码模型的显著性着手,指出引起局部线性编码模型中单个权重系数不显著的根本原因为K个近邻视觉单词之间的复共线性。为此,给出主成分估计的改进办法,该方法解决了复共线性带来的不稳定、均方误差大的问题。在Caltech-4图像数据集上的视觉物体分类实验结果表明,该方法提高了视觉物体分类正确率。(2)提出了基于视觉词汇主成分的局部约束线性编码方法。通过分析发现局部约束主成分线性编码方法在解决复共线性问题的同时也增加了编码过程的时间开销。针对该问题,本文将对于每个局部特征的K个近邻视觉单词的主成分的确定简化成对视觉词汇只做一次主成分确定,视觉词汇主成分的数量根据累积贡献率而定。实验结果表明,基于视觉词汇主成分的局部约束线性编码方法将线性编码时间减少了1/3；当累积贡献率为85%,视觉词汇主成分个数为20时,基于视觉词汇主成分的局部约束线性编码方法的性能是最优的,综合考虑了编码时间和分类效果；本文提出的两种线性编码方法在每种池化方式下都提高了分类正确率,并且它们的分类正确率相近,证明基于视觉词汇主成分的局部约束线性编码方法在减少时间开销的同时,保留了局部约束主成分线性编码方法的优点。(3)针对目前局部特征建模中引入空间位置信息的空间金字塔匹配方法缺乏对图像中视觉物体平移、缩放和旋转的考虑,本文提出一种基于视觉词汇形状描述模型的空间匹配方法。该方法相对于每个视觉单词的几何中心建立空间几何模型,保证平移不变性；给出对数极坐标空间金字塔匹配,对对数极半径和极角做等比例划分,保证缩放不变性；并在空间金字塔划分过程中确定极角的主方向,从而保证旋转不变性。分别在Caltech-4数据集和自建图像数据集上对该方法进行了验证和比较。实验结果表明,该方法提高了分类识别正确率,特别是对于包含明显平移、缩放和旋转变化的图像数据集；而且该方法的方差变小,说明其鲁棒性更强。(4)在研究局部特征线性编码方法的基础上,讨论局部特征编码的池化方法,引进指标规范化。在Caltech-4数据集上的实验结果表明,引入的指标规范化降低了局部特征描述模型向量的稀疏性；明确了局部特征描述模型向量的稀疏程度对分类结果的重要影响。(5)综合本文提出的有关局部特征建模的方法进行综合实验。Matlab环境中的实验结果表明,Caltech-101图像数据集上本文方法的平均分类正确率(ACA)高于其它5种流行方法；Pascal VOC2007图像数据集上本文方法在没有考虑多种局部特征融合以及采用线性SVM分类器的情况下,平均查准率(AP)接近于Pascal VOC2007竞赛的最好分类结果。最后实现了一个基于C/S架构的视觉物体分类系统原型。更多还原

【Abstract】 Visual object categorization is a very challenging key technology in the application of image retrieval, massive video data, visual perception alternative, automatic robot, interactive games, and etc. It has a wide range of application needs and significant value both in applications and in researches. The research goals of visual object categorization are to detect objects in images and to determine the object’s categories. This dissertation studies local feature modeling for visual object categorization using bag-of-visual-words method, including local feature linear coding, pooling and spatial matching scheme. Some improved methods are proposed. The main work and innovation are presented as following:(1) Locality-constrained principal component linear coding (LPLC) is proposed. Through the linear correlation analysis between the local feature and its K-nearest-neighbor visual words and significance testing of locality-constrained linear coding, this dissertation finds that the fundamental reason for causing nonsignificance of the weight coefficient is the multicollinearity of K-nearest-neighbor visual words. So LPLC is presented. Experiments have been conducted for comparing and evaluating the proposed method utilizing the Caltech-4dataset. Experimental results show that the proposed method improves the classification accuracy.(2) Locality-constrained linear coding based on the principal components of visual vocabulary is proposed. LPLC solves the multicollinearity, but it increases the time overhead of the coding. To determine the principal components of K-nearest-neighbor visual words of each local feature is simplified to only determine the principal components of visual vocabulary. The number of principal components of the visual vocabulary is determined according to the cumulative contribution ratio. Experimental results show that linear encoding time is reduced by1/3using the proposed method; in the case of the comprehensive consideration of coding time and classification results, the proposed method is optimal when the cumulative contribution ratio is85%as well as the number of principal components of the visual vocabulary is20; in the each pooling way, the two proposed linear coding methods all improve the classification accuracy and their classification accuracy results are similar, indicating that locality-constrained linear coding based on the principal components of visual vocabulary reduces the time overhead and the same time it retains the advantages of LPLC.(3) The spatial pyramid matching (SPM) approach, which is based on approximate global geometric correspondence, disregards invariance to translation, scale and rotation of visual objects in images. A novel spatial matching method based on visual vocabulary shape description model is proposed. According to this method, spatial geometric model relative to the geometric center of each visual word is constructed to guarantee translation invariance; Log polar spatial pyramid matching is presented, log polar radius and polar angle are subdivided in proportion and a consistent orientation to visual word is assigned in order to achieve scaling and rotation invariance. Experiments have been conducted for comparing and evaluating the proposed method utilizing the Caltech-4dataset and our own dataset. Experimental results show that the proposed method improves the classification accuracy, especially for the dataset containing images with obvious translation, scaling and rotation changes, and is more robust because of its smaller variance.(4) On the basis of studying locality-constrained linear coding, discuss the pooling of local feature codes and introduce power normalization into local feature modeling. Experiments have been conducted for comparing and evaluating the proposed methods utilizing the Caltech-4dataset. Experimental results show that the introduced power normalization reduces the sparsity of local feature model vector, and realize that the sparsity of local feature model vector can significantly affect classification results.(5) Integrate the proposed methods related to local feature modeling. Experiments have been conducted for comparing and evaluating the integrated method utilizing the Caltech-101dataset and Pascal VOC2007dataset and using Matlab. Experimental results on Caltech-101dataset show that average classification accuracy (ACA) of the integrated method is higher than the other five methods. Experimental results on Caltech-101dataset show that average precision (AP) of the integrated method is similar to the best system in Pascal VOC2007challenge. Finally, we implement a visual object categorization system prototype based on C/S.更多还原