基于颜色距离与Edge Boxes候选区域算法

王春哲; 安军社; 姜秀杰; 邢笑雪; 崔天舒

doi:10.3788/YJYXS20193407.0698

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基于颜色距离与Edge Boxes候选区域算法

图像处理 | 更新时间：2020-07-05

- 基于颜色距离与Edge Boxes候选区域算法
- Region proposals algorithm based on color distance and Edge Boxes
- 液晶与显示 2019年34卷第7期页码：698-707
- 作者机构：
  
  1.中国科学院国家空间科学中心复杂航天系统电子信息技术重点实验室, 北京 100190
  2.中国科学院大学, 北京 100049
  3.长春大学, 吉林长春 130022
- 作者简介：
  
  [ "王春哲(1989-), 男, 吉林松原人, 博士研究生, 2015年于长春理工大学获得硕士学位, 主要从事深度学习及目标检测方面的研究。E-mail:wangchunzhe163@sina.com" ]
  [ "安军社(1969-), 男, 陕西渭南人, 博士, 研究员, 2004年于西北工业大学获得博士学位, 主要从事空间飞行器综合电子系统及深度学习方面的研究。E-mail:anjunshe@nssc.ac.cn" ]
- 基金信息：
  
  国家自然科学基金(61805021);中国科学院复杂航天系统电子信息技术重点实验室自主部署基金(Y42613A32S);National Natural Science Foundation of China(61805021);Self-deployment funds of Key Laboratory of Electronics and Information Technology for Space Systems Chinese Academy of Sciences(Y42613A32S)
- DOI：10.3788/YJYXS20193407.0698
  中图分类号： TP394.1
- 收稿日期：2019-01-30，
  
  录用日期：2019-4-29，
  
  纸质出版日期：2019-07-05
- 稿件说明：
移动端阅览
王春哲, 安军社, 姜秀杰, 等. 基于颜色距离与Edge Boxes候选区域算法[J]. 液晶与显示, 2019,34(7):698-707.

Chun-zhe WANG, Jun-she AN, Xiu-jie JIANG, et al. Region proposals algorithm based on color distance and Edge Boxes[J]. Chinese journal of liquid crystals and displays, 2019, 34(7): 698-707.
王春哲, 安军社, 姜秀杰, 等. 基于颜色距离与Edge Boxes候选区域算法[J]. 液晶与显示, 2019,34(7):698-707. DOI： 10.3788/YJYXS20193407.0698.

Chun-zhe WANG, Jun-she AN, Xiu-jie JIANG, et al. Region proposals algorithm based on color distance and Edge Boxes[J]. Chinese journal of liquid crystals and displays, 2019, 34(7): 698-707. DOI： 10.3788/YJYXS20193407.0698.

摘要

针对Edge Boxes算法召回率不高的问题，并结合目标的显著性检测，提出了一种基于颜色距离与Edge Boxes候选区域算法。首先利用结构化边缘检测算子获取图像的边缘特征，并通过边缘点聚合及边缘段相似性策略，获取每个边缘段的权值；其次，在待检测图像上无重叠采样若干图像块，记作C图像块，并将C图像块向周边延拓像素，获取S图像块；然后，根据颜色直方图，计算两图像块各颜色通道的卡方距离，并赋予合适权重作为该C图像块的显著性得分；最后，统计滑动窗口内边缘段的数量和C图像块数，确定候选区域。在PASCAL VOC 2007验证集上实验，当交并比取0.5，0.6，0.7，候选区域个数为2 000时，与Edge Boxes相比，所提算法的召回率分别提高了0.46%，0.35%，0.57%。每张图像的运行时间大约为0.43 s，这表明，所提算法以牺牲微小计算资源却能够有效改善候选区域质量。

Abstract

For the low recall rate of Edge Boxes algorithm

a region proposals algorithm based on color distance and Edge Boxes is proposed in this paper

combining with the object saliency detection. Firstly

the edge features are computed using Structured Edge detector

and the weight values of every edge group are obtained through edge point aggregation and edge groups affinities. Secondly

several image patches are sampled randomly in the image

which is to be detected

this image patches are denoted C image patches and the S image patches are obtained through expanding the pixels of C image patches. The Chi-square distance of this two image patches are computed according to the histogram of each channel of C and S image patches

the salient scores of this image patch are obtained with putting appropriate weight value on chi-square distance of three channels. Finally

the region proposals are determined with the number of edge groups and C patches in sliding windows. The experimental results in PASCAL VOC 2007 validation set show that the object recall of the proposed algorithm increases 0.46%

0.35%

0.57% than Edge Boxes at intersection over union threshold of 0.5

0.6

0.7 in 2 000 region proposals

respectively. Our approach runs about 0.43 s in an image

this demonstrates that the proposed algorithm can effectively improve the quality of region proposals with only minor loss in computation resources.

关键词

Keywords

references

HOSANG J, BENENSON R, DOLLÁR P, et al . What makes for effective detection proposals?[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2016, 38(4): 814-830.

李宇, 刘雪莹, 张洪群, 等.基于卷积神经网络的光学遥感图像检索[J].光学精密工程, 2018, 26(1):200-207.

LI Y, LIU X Y, ZHANG H Q, et al . Optical remote sensing image retrieval based on convolutional neural networks[J]. Optics and Precision Engineering , 2018, 26(1): 200-207. (in Chinese)

梁华, 宋玉龙, 钱锋, 等.基于深度学习的航空对地小目标检测[J].液晶与显示, 2018, 33(9):793-800.

LIANG H, SONG Y L, QIAN F, et al . Detection of small target in aerial photography based on deep learning[J]. Chinese Journal of Liquid Crystals and Displays , 2018, 33(9): 793-800. (in Chinese)

UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al . Selective search for object recognition[J]. International Journal of Computer Vision , 2013, 104(2): 154-171.

CARREIRA J, SMINCHISESCU C. CPMC: automatic object segmentation using constrained parametric min-cuts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2012, 34(7): 1312-1328.

ALEXE B, DESELAERS T, FERRARI V. Measuring the objectness of image windows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2012, 34(11): 2189-2202.

CHENG MM, ZHANG Z M, LIN W Y, et al . BING: binarized normed gradients for objectness estimation at 300fps[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus, OH, USA: IEEE, 2014: 3286-3293.

邝沛江.基于改进Edge Boxes的物体检测算法的研究[D].广州: 华南理工大学, 2017. http://cdmd.cnki.com.cn/Article/CDMD-10561-1017733466.htm

KUANG P J. Research on algorithm in object detection based on improved Edge Boxes[D]. Guangzhou: South China University of Technology, 2017. (in Chinese)

ZITNICK C L, DOLLÁR P. Edge Boxes: locating object proposals from edges[C]// Proceedings of the 13 th European Conference on Computer Vision . Zurich, Switzerland: Springer, 2014: 391-405.

LIU T, SUN J, ZHENG N N, et al . Learning to detect a salient object[C]// Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition . Minneapolis, MN, USA: IEEE, 2007: 1-8.

BRUCE N D B, TSOTSOS J K. Saliency based on information maximization[C]// Proceedings of the 18 th International Conference on Neural Information Processing Systems . Vancouver, British Columbia, Canada: ACM, 2005: 155-162.

ISHIKURA K, KURITA N, CHANDLER D M, et al . Saliency detection based on multiscale extrema of local perceptual color differences[J]. IEEE Transactions on Image Processing , 2018, 27(2): 703-717.

NOVEMBRE G, PAWAR V M, BUFACCHI R J, et al . Saliency detection as a reactive process: unexpected sensory events evoke corticomuscular coupling[J]. The Journal of Neuroscience , 2018, 38(9): 2385-2397.

王慧利, 朱明, 蔺春波, 等.光学遥感图像中复杂海背景下的舰船检测[J].光学精密工程, 2018, 26(3):723-732.

WANG H L, ZHU M, LIN C B, et al . Ship detection of complex sea background in optical remote sensing images[J]. Optics and Precision Engineering , 2018, 26(3): 723-732. (in Chinese)

丁鹏, 张叶, 贾平, 等.基于视觉显著性的海面舰船检测技术[J].电子学报, 2018, 46(1):127-134.

DING P, ZHANG Y, JIA P, et al . Ship detection on sea surface based on visual saliency[J]. Acta Electronica Sinica , 2018, 46(1): 127-134. (in Chinese)

MANEN S, GUILLAUMIN M, VAN GOOL L. Prime object proposals with randomized prim's algorithm[C]// Proceedings of 2013 IEEE International Conference on Computer Vision . Sydney, NSW, Australia: IEEE, 2013: 2536-2543.

CHEN X Z, MA H M, WANG X, et al . Improving object proposals with multi-thresholding straddling expansion[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston, MA, USA: IEEE, 2015: 2587-2595.

RANTALANKILA P, KANNALA J, RAHTU E. Generating object segmentation proposals using global and local search[C]/ /Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus, OH, USA: IEEE, 2014: 2417-2424.

PONT-TUSET J, ARBELÁEZ P, BARRON J T, et al . Multiscale combinatorial grouping for image segmentation and object proposal generation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017, 39(1): 128-140.

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