|Table of Contents|

Human silhouette identification based on Gabor feature and convolutional neural network(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年01期
Page:
89-
Research Field:
Publishing date:

Info

Title:
Human silhouette identification based on Gabor feature and convolutional neural network
Author(s):
Wang LinDong Nan
School of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,China
Keywords:
human silhouette identification Gabor feature convolutional neural network picture processing deep learning VOC2012 data set
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2018.42.01.013
Abstract:
Traditional methods can not divide the human silhouette accurately in a single image. Aiming at this problem,a method of human silhouette extraction based on Gabor feature and convolutional neural network(CNN)is proposed. Firstly the Gabor wavelet feature of human image in 8 directions is calculated and introduced into the improved CNN for the character model training; secondly the being-tested image is introduced into character model for detection after calculating Gabor wavelet feature,and the mask of the characters is output; finally the mask is processed by morphology and an operation is made with the original image to get the human silhouette. The detection accuracy of this method is up to 96% in the test of VOC2012 data set. The experimental results show that the combination of traditional feature extraction method and the feature learning method of deep learning improves the speed of feature learning and the detection accuracy.

References:

[1] Cai Z,Saberian M,Vasconcelos N. Learning complexity-aware cascades for deep pedestrian detection[C]//IEEE International Conference on Computer Vision. Santiago,Chile:IEEE,2015:3361-3369. [2]Oren M,Papageorgiou C,Sinha P,et al. Pedestrian detection using wavelet templates[C]//Conference on Computer Vision and Pattern Recognition. San Juan,Puerto Rico,USA:IEEE Computer Society,1997:193. [3]Hosang J,Benenson R,Dollar P,et al. What makes for effective detection proposals?[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2016,38(4):814-830. [4]Dalal N,Triggs B. Histograms of oriented gradients for human detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005. San Diego,CA,USA:IEEE,2005:886-893. [5]Zhang S,Bauckhage C,Cremers A B. Informed Haar-like features improve pedestrian detection[C]//Computer Vision and Pattern Recognition. Columbus,OH,USA:IEEE,2014:947-954. [6]Dollár P,Tu Z,Perona P,et al. Integral channel features[C]//BMVC 2009:Proceedings of British Machine Vision Conference. London,UK:BMVA Press,2009:1-11. [7]Liu C,Wechsler H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J]. IEEE Transactions on Image Processing,2002,11(4):467-475. [8]Maji S,Berg A C,Malik J. Classification using intersection kernel support vector machines is efficient[C]//IEEE Conference on Computer Vision and Pattern Recognition,2008. Anchorage,AK,USA:IEEE,2008:1-8. [9]FreundY,Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting[C]//Computational Learning Theory. Berlin:Springer Berlin Heidelberg,1995:119-139. [10]Chappelow J. Snakes:active contour models[J]. International Journal of Computer Vision,1988,1(4):321-331. [11]曹之江,郝矿荣,丁永生. 基于GVF-Snake人体轮廓提取的优化算法[J]. 计算机工程,2008,34(22):204-206. Cao Z J,Hao K R,Ding Y S. Optimization algorithm based on GVF-snake for body contour extraction[J]. Computer Engineering,2008,34(22):204-206. [12]Liu T,Yuan Z,Sun J,et al. Learning to detect a salient object.[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,33(2):353-367. [13]逯暄,肖泽龙,胡泰洋,等. 基于视觉显著性分析的毫米波辐射图像增强[J]. 南京理工大学学报,2014,38(1):134-139. Lu Xuan,Xiao Zelong,Hu Taiyang,et al. MMW radiometric image enhancement based on visual saliency analysis[J]. Journal of Nanjing University of Science and Technology,2014,38(1):134-139. [14]Hinton G,Salakhutdinov R. Reducing the dimensionality of data with neural networks[J].Science,2006,313:504-507. [15]Redmon J,Angelova A. Real-time grasp detection using convolutional neural networks[C]//IEEE International Conference on Robotics and Automation. Piscotaway,USA:IEEE,2015:1316-1322. [16]Long J,Shelhamer E,Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,39(4):640-651. [17]姚伟,孙正兴,张岩. 面向脸部表情识别的Gabor特征选择方法[J]. 计算机辅助设计与图形学学报,2008,20(1):79-84. Yao W,Sun Z X,Zhang Y. Optimal Gabor feature for facial expression recognition[J]. Journal of Computer-Aided Design & Computer Graphics,2008,20(1):79-84. [18]Arivazhagan S,Ganesan L,Priyal S P. Texture classification using Gabor wavelets based rotation invariant features[M]. New York,NY,USA:Elsevier Science Inc,2006. [19]Krizhevsky A,Sutskever I,Hinton G E. ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems. New York,USA:Curran Associates Inc,2012:1097-1105. [20]Szegedy C,Liu W,Jia Y,et al. Going deeper with convolutions[C]//Computer Vision and Pattern Recognition. Boston,MA,USA:IEEE,2015:1-9. [21]Simonyan K,Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556,2014.

Memo

Memo:
-
Last Update: 2018-02-28