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目标鲁棒识别的抗旋转HDO局部特征描述

胡扬 张东波 段琪

胡扬, 张东波, 段琪. 目标鲁棒识别的抗旋转HDO局部特征描述. 自动化学报, 2017, 43(4): 665-673. doi: 10.16383/j.aas.2017.c150837
引用本文: 胡扬, 张东波, 段琪. 目标鲁棒识别的抗旋转HDO局部特征描述. 自动化学报, 2017, 43(4): 665-673. doi: 10.16383/j.aas.2017.c150837
HU Yang, ZHANG Dong-Bo, DUAN Qi. An Improved Rotation-invariant HDO Local Description for Object Recognition. ACTA AUTOMATICA SINICA, 2017, 43(4): 665-673. doi: 10.16383/j.aas.2017.c150837
Citation: HU Yang, ZHANG Dong-Bo, DUAN Qi. An Improved Rotation-invariant HDO Local Description for Object Recognition. ACTA AUTOMATICA SINICA, 2017, 43(4): 665-673. doi: 10.16383/j.aas.2017.c150837

目标鲁棒识别的抗旋转HDO局部特征描述

doi: 10.16383/j.aas.2017.c150837
基金项目: 

湖南省自然科学基金 2017JJ2251

详细信息
    作者简介:

    胡扬 湘潭大学信息工程学院硕士研究生.主要研究方向为图像处理与模式识别.E-mail:15574466364@163.com

    段琪 湘潭大学信息工程学院硕士研究生.主要研究方向为图像处理与模式识别.E-mail:xtuduanqi@163.com

    通讯作者:

    ZHANG Dong-Bo Professor at the College of Information Engineering, Xiangtan University. He received his Ph. D. degree in control science and technology from Hunan University in 2007. His research interest covers digital image processing, pattern recognition, and machine learning. Corresponding author of this paper

An Improved Rotation-invariant HDO Local Description for Object Recognition

Funds: 

Hunan Province Nature Science Funding 2017JJ2251

More Information
    Author Bio:

    Master student at the College of Information Engineering, Xiangtan University. His research interest covers image processing and pattern recognition

    Master student at the College of Information Engineering, Xiangtan University. His research interest covers image processing and pattern recognition

  • 摘要: 主方向直方图(Histograms of dominant orientations,HDO)是一种简单但性能优良的局部图像描述子,但是,原有的HDO特征描述不具备旋转不变性.本文提出一种抗旋转变换HDO特征描述方法,在进行RGT(Radial gradient transform)变换后,采用圆形邻域计算给定位置的结构张量,使得求取的主方向和一致性特征分量具备一定的旋转不变性,最后为增强辨别能力,采用了多扇区划分空间池化操作.在公开的MIT人脸数据集中的测试结果显示,如果图片不旋转,本文方法准确率与传统的HDO算法基本持平,达到92.10%,但当样本图片旋转后,本文算法准确率比传统HDO算法高10.36%.此外,在行人数据集、合成的旋转手掌和旋转人脸识别实验中,本文方法的检测结果也明显优于传统的HDO算法.另外本文方法在53Objects、ZuBuD和Kentuky三个数据集上的识别性能也优于大部分现有抗旋转算子.
  • 图  1  图像单元 (Cell)、图像块 (Block) 和原始图像 (Image) 的对应关系

    Fig.  1  The diagram of cell, block and image

    图  2  图像Block主方向直方图

    Fig.  2  Histogram of dominant orientation of image block

    图  3  RGT变换示意图

    Fig.  3  Schematic diagram of RGT transform

    图  4  RGT变换及近似RGT变换

    Fig.  4  RGT transform and approximate RGT transform

    图  5  旋转后的方形邻域和圆形邻域示意图

    Fig.  5  Schematic diagram of circular neighborhood and rectangular neighborhood after rotation

    图  6  图像的环状空间划分

    Fig.  6  Annular division of image

    图  7  四扇区环状空间划分

    Fig.  7  Four sections annular division of image

    图  8  某测试图片旋转时的相关度计算示意图和相关度曲线

    Fig.  8  Schematic diagram and curve of correlation calculation of example test image rotation

    图  9  样本图片示例

    Fig.  9  Sample picture

    图  10  原HDO特征与改进HDO环形分区特征对人脸旋转图片检测结果

    Fig.  10  Test result in rotation image of original HDO and improved HDO with annular division

    图  11  合成旋转人脸检测示例

    Fig.  11  Diagram of composite rotating face detection

    图  12  合成旋转手掌检测示例

    Fig.  12  Diagram of composite rotating palm detection

    表  1  图 9样本图片旋转的平均相关度计算值

    Table  1  Fig. 9 sample average correlation calculation value of the image rotation

    图片旋转角度 30 45 60 90 120 135 150 180 210 225 240 270 300 330 平均值 标准差
    原HDO 0.47 0.28 0.46 0.95 0.44 0.30 0.42 0.97 0.46 0.28 0.44 0.96 0.44 0.44 0.52 0.2465
    环形分区 0.78 0.88 0.75 0.88 0.77 0.81 0.77 0.83 0.77 0.84 0.84 0.86 0.75 0.79 0.81 0.0454
    环形加扇形四分区 0.73 0.78 0.72 0.71 0.70 0.71 0.69 0.68 0.69 0.73 0.69 0.72 0.72 0.76 0.72 0.0279
    环形加扇形八分区 0.68 0.70 0.62 0.59 0.62 0.62 0.62 0.61 0.60 0.63 0.59 0.60 0.64 0.72 0.63 0.0405
    下载: 导出CSV

    表  2  传统HDO和改进HDO人脸分类性能比较 (%)

    Table  2  Classification performance comparison between original and improved HDO ({\%)

    分类准确率 传统HDO 环形分区 4扇区环状空间划分 8扇区环状空间划分 16扇区环状空间划分
    训练图片不旋转
    测试图片不旋转
    92.36 81.72 92.11 93.74 93.82
    训练图片不旋转
    测试图片旋转
    64.65 71.18 74.98 75.96 76.18
    训练图片旋转
    测试图片旋转
    70.78 74.20 74.25 74.05 74.14
    下载: 导出CSV

    表  3  传统HDO和改进HDO行人分类性能比较 (%)

    Table  3  Comparison of pedestrian classification perfor-mance between original HDO and improved HDO (%)

    分类准确率 传统HDO 环形分区 4扇区环状空间划分 8扇区环状空间划分 16扇区环状空间划分
    训练图片不旋转
    测试图片不旋转
    85.00 85.67 84.33 89.33 91.33
    训练图片不旋转
    测试图片旋转
    68.67 69.00 70.00 72.67 74.67
    训练图片旋转
    测试图片旋转
    76.67 78.67 79.67 80.33 89.67
    下载: 导出CSV

    表  4  传统HDO和改进HDO手掌分类性能比较 (%)

    Table  4  Comparison of palm classification performance between original HDO and improved HDO (%)

    算法 传统HDO 环形分区 4扇区环状空间划分 8扇区环状空间划分 16扇区环状空间划分
    分类准确率 74.63 78.86 80.07 80.33 83.36
    下载: 导出CSV

    表  5  图像分块大小分类性能比较 (%)

    Table  5  The image block size comparison on the performances of classification (%)

    分类准确率 划分方式 环形分区 4扇区环状空间划分 8扇区环状空间划分 16扇区环状空间划分
    训练图片不旋转 AW = 1 71.18 74.98 75.96 76.94
    测试图片旋转 AW = 2 70.68 74.81 74.78 75.48
    下载: 导出CSV

    表  6  特征提取时间

    Table  6  Feature extraction time

    特征描述子 传统HDO 环形分区 4扇区环状空间划分 8扇区环状空间划分 16扇区环状空间划分
    特征维度 968 72 216 504 1 080
    提取时间 (ms) 746.5 362.4 458.5 648.4 702.5
    下载: 导出CSV

    表  7  不同局部描述子在三种数据集上的识别率比较 (%)

    Table  7  Comparison of different local descriptor in the recognition rate on the three data sets (%)

    Descriptor RIFT SIFT DAISY MRRID MROGH HDO 4扇区环状空间划分HDO
    53Objects Accuracy 37.0 52.2 61.2 57.4 72.5 56.6 68.2
    ZuBuD Accuracy 66.8 75.5 83.1 78.6 88.1 72.7 83.6
    Kentuky Accuracy 34.1 48.2 58.3 57.5 74.0 62.3 75.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-12-11
  • 录用日期:  2016-05-23
  • 刊出日期:  2017-04-20

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