Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (12): 3409-3413.DOI: 10.11772/j.issn.1001-9081.2018040892

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Learning method of indoor scene semantic annotation based on texture information

ZHANG Yuanyuan1,2, HUANG Yijun1, WANG Yuefei1   

  1. 1. School of Mechanical and Marine Engineering, Qinzhou University, Qinzhou Guangxi 535011, China;
    2. Qinzhou Key Laboratory for Advanced Technology to Internet of Things, Qinzhou Guangxi 535011, China
  • Received:2018-05-02 Revised:2018-07-19 Online:2018-12-10 Published:2018-12-15
  • Contact: 张圆圆
  • Supported by:
    This work is partially supported by the Project of Improving the Basic Ability of Young and Middle-Aged Teachers in Universities of Guangxi (17KY0793), the Key Laboratory for Design, Manufacture and Control of Marine Machinery and Equipment in Guangxi Universities (GXLH2014ZD-05, GXLH2016YB-07), the Key Laboratory of Advanced Technology for the Internet of Things of Qinzhou (IOT2018C02).


张圆圆1,2, 黄宜军1, 王跃飞1   

  1. 1. 钦州学院 机械与船舶海洋工程学院, 广西 钦州 535011;
    2. 钦州市物联网先进技术重点实验室, 广西 钦州 535011
  • 通讯作者: 张圆圆
  • 作者简介:张圆圆(1987-),女,河南民权人,讲师,硕士,主要研究方向:智能化检测与控制、模式识别;黄宜军(1965-),男,广西宜州人,教授,博士,主要研究方向:模式识别、智能控制;王跃飞(1986-),男,山西偏关人,助教,硕士,主要研究方向:复杂工业控制综合自动化。
  • 基金资助:

Abstract: The manual processing method is mainly used for the detection, tracking and information editing of key objects in indoor scene video, which has the problems of low efficiency and low precision. In order to solve the problems, a new learning method of indoor scene semantic annotation based on texture information was proposed. Firstly, the optical flow method was used to obtain the motion information between video frames, and the key frame annotation and interframe motion information were used to initialize the annotation of non-key frames. Then, the image texture information constraint of non-key frames and its initialized annotation were used to construct an energy equation. Finally, the graph-cuts method was used for optimizing to obtain the solution of the energy equation, which was the non-key frame semantic annotation. The experimental results of the annotation accuracy and visual effects show that, compared with the motion estimation method and the model-based learning method, the proposed learning method of indoor scene semantic annotation based on texture information has the better effect. The proposed method can provide the reference for low-latency decision-making systems such as service robots, smart home and emergency response.

Key words: indoor scene, semantic annotation learning, motion estimation, image texture, graph-cuts

摘要: 针对目前室内场景视频中关键物体的检测、跟踪及信息编辑等方面主要是采用人工处理方式,存在效率低、精度不高等问题,提出了一种基于纹理信息的室内场景语义标注学习方法。首先,采用光流方法获取视频帧间的运动信息,利用关键帧标注和帧间运动信息进行非关键帧的标注初始化;然后,利用非关键帧的图像纹理信息约束及其初始化标注构建能量方程;最后,利用图割方法优化得到该能量方程的解,即为非关键帧语义标注。标注的准确率和视觉效果的实验结果表明,与运动估计法和基于模型的学习法相比较,所提基于纹理信息的室内场景语义标注学习法具有较好的效果。该方法可以为服务机器人、智能家居、应急响应等低时延决策系统提供参考。

关键词: 室内场景, 语义标注学习, 运动估计, 图像纹理, 图割

CLC Number: