交通场景的多视觉特征图像分割方法

doi:10.3969/j.issn.1001-2400.2015.06.003

J4 ›› 2015, Vol. 42 ›› Issue (6): 11-16.doi: 10.3969/j.issn.1001-2400.2015.06.003

交通场景的多视觉特征图像分割方法

邓燕子;卢朝阳;李静

(西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071)

收稿日期:2014-11-24 出版日期:2015-12-20 发布日期:2016-01-25
作者简介:邓燕子(1983-)，女，西安电子科技大学博士研究生，E-mail: dyzamour@163.com．
基金资助:
中央高校基本科研业务费专项资金资助项目(K50510010007)

Segmentation of the image with multi-visual features for a traffic scene

DENG Yanzi;LU Zhaoyang;LI Jing

(State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an 710071, China)

Received:2014-11-24 Online:2015-12-20 Published:2016-01-25

摘要/Abstract

摘要：

针对场景分割中基于像素分类计算较为复杂，使用特征类别较少难以提高分类精度的缺点，提出一种新的基于超像素多种视觉特征来学习场景几何结构类别的模型．首先，在图像超像素基础上进行多视觉特征提取；然后，利用这些特征对超像素进行分类，再计算相邻超像素视觉特征的差异，推断相邻超像素类别的一致性；最后，用初始分类和一致性分类结果构造基于马尔科夫随机场模型的能量函数，使用基于图割的优化方法确定超像素的类别．实验结果表明，该方法对特征的选择以及分类优化算法能够有效提高分类的精度，对交通场景能够实现较好的分割效果．

关键词: 场景分割算法, 超像素, 多视觉特征提取, 随机森林回归, 马尔科夫随机场

Abstract:

Scene segmentations based on the pixel classifying calculation are complicated, and they use insufficient features, thus resulting in a low accuracy, so a new model is proposed to overcome these shortcomings,which is to learn these geometric classes based on multi-visual features of super-pixels. First, various features are extracted from the super-pixels of an input image. These features are used for classifying the super-pixels. Then the difference between the adjacent super-pixels is calculated to predict their consistency. The initial classification result and the consistency are synthesized to the Markov Random Field energy function, which is then minimized based on the graph-cuts algorithm to get the final labels of the super-pixels. Experimental results prove the effectiveness of the multi-visual features and the optimization method proposed, with superior performance achieved for traffic scenes.

Key words: scene segmentation algorithm, super-pixels, multi-visual feature extraction, random forest regression, Markov random fields

邓燕子;卢朝阳;李静. 交通场景的多视觉特征图像分割方法[J]. J4, 2015, 42(6): 11-16.

DENG Yanzi;LU Zhaoyang;LI Jing. Segmentation of the image with multi-visual features for a traffic scene[J]. J4, 2015, 42(6): 11-16.

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交通场景的多视觉特征图像分割方法

Segmentation of the image with multi-visual features for a traffic scene

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