节点文献
面向交通场景的图像分类研究
Research on Traffic Scene Oriented Image Classification
【作者】 谷志敏；
【导师】 彭强；
【作者基本信息】 西南交通大学 ， 计算机技术， 2012， 硕士
【摘要】 随着智能交通监控技术迅速发展,交通监控图像和视频数量飞速增长。仅依靠人工分析海量的交通图像和视频费时耗力,智能地快速检索和有效管理海量的交通图像和视频正面临着巨大的挑战。面向交通场景的图像分类是智能检索和管理交通图像和视频的基础,也是实现智能监控要解决的关键技术之一,它的研究具有理论价值和应用价值。本文的主要目标是实现交通场景图像的分类,围绕交通场景图像特征提取,图像表述及其分类展开研究。主要内容如下：第一,本文提取局部二值模式图像低层特征,采用支持向量机分类器进行图像分类,实验结果并不能达到预期的效果,其主要原因是在复杂背景下,图像的低层特征不能很好地描述图像语义内容。视觉词汇模型能描述图像的中层语义特征。因此本文提取图像SIFT特征,形成图像的视觉词汇表述,并使用支持向量机进行交通场景的图像分类。比较两种方法的实验结果,基于SIFT的视觉词汇模型的图像分类准确率较高。第二,视觉词汇模型忽略了图像的空间信息,本文引入空间金字塔模型对图像进行表述,该模型是对视觉词汇模型的改进,在图像特征空间上结合了图像块的上下文信息。采用这种图像表述并结合支持向量机分类器进行交通场景的图像分类,与视觉词汇模型法作比较,图像分类准确率有显著提高。第三,传统空间金字塔模型中向量量化误差较大,并且基于它的图像分类运算复杂度高,运行时间长。为了解决这个问题,本文引入局部线性编码改进向量量化编码,采用这种图像表述并结合Liblinear分类器进行交通场景的图像分类,该方法降低了图像分类运算复杂度和运行时间,提高了分类准确率。
【Abstract】 With the flourish of intelligent traffic monitoring technology, it brings the number of traffic surveillance images and videos growing rapidly. It is time-consuming and labor-intensive to analysis all the videos manually. Intelligently fast retrieving and managing traffic images/videos are facing a great challenge. Traffic scene oriented image classification is the ground for traffic image/video intelligently retrieval and management, and it is one of the key technologies to be solved in realizing intelligent monitoring. So the research of traffic scene oriented image classification has theoretical and practical value.The main goal is realizing the traffic scene oriented image classification. This thesis focuses on image feature extraction, image representation and classification. The main research contents of this thesis are as follows:Firstly, Local Binary Pattern based image low-level feature is extracted in thesis. Then support vector machine is adopted to realize the traffic scene oriented image classification. The experimental results do not achieve the desired effect. The main reason is low-level features of images can’t describe image semantic content very well. Visual words can describe image middle-level semantic content. So SIFT is used in this thesis to form the visual words representation of image and support vector machine is adopted to realize the traffic scene oriented image classification. Through comparing experiment results of the two methods, the image classification performance of SIFT based visual words model is better.Secondly, the visual words model ignores the spatial information of image. Spatial pyramid matching model is introduced to represent the images in this thesis. This model which makes use of the image block context in image feature space is the improvement of visual words. This image statement combining with support vector machine is used for traffic scene oriented image classification. Compared with visual words model, the precision is improved significantly.Thirdly. The vector quantization of traditional spatial pyramid model has large quantization errors. The computing complexity of spatial pyramid matching based image classification is relatively high and the run time is too long. In order to solve these problems, locality-constrained linear coding method is introduced to improve the vector quantization coding. This image statement combining with Liblinear classifier is used for traffic scene oriented image classification. This method reduces the computing complexity and running time and improves image classification precision.