[1]YAN Han,ZHANG Xuxiu,ZHANG Jingdan.Image recognition method based on multi-perceptual interest region feature fusion[J].CAAI Transactions on Intelligent Systems,2021,16(2):263-270.[doi:10.11992/tis.201906032]
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Image recognition method based on multi-perceptual interest region feature fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 263-270 Column: 学术论文—机器感知与模式识别 Public date: 2021-03-05
Title:
Image recognition method based on multi-perceptual interest region feature fusion
Author(s):
YAN Han;  ZHANG Xuxiu;  ZHANG Jingdan
School of Electrical Information Engineering, Dalian Jiaotong University, Dalian 116028, China
Keywords:
deep learning; image recognition; migration learning; feature fusion; integrated learning; feature extraction; CAM visualization; VGGNet; ResNet
CLC:
TP311
DOI:
10.11992/tis.201906032
Abstract:
This paper presents the deep convolution neural network fusion mechanism and proposes an image recognition method based on multi-perceptual interest region feature fusion in combination with the deep-migration learning method. This is to solve the problem of different deep-learning models used on different interest regions when they recognize a natural image. The migration learning method is applied to the convolution neural net architectures, namely VGG and ResNet networks. Then, through the visualization of the heat map and the features of single classification model, a conclusion is drawn that the characteristic regions associated with different network models are different. Based on this, the methods of feature splicing, feature fusion and splicing, and fusion voting systems are designed to fuse different model features, obtaining three new fusion models. The experimental results show that the recognition accuracy of this method on Kaggle dataset is higher than that of VGG-16, VGG-19, ResNet-50, and DenseNet-201 models.
References:
[1] SPENCER JR B F, HOSKERE V, NARAZAKI Y. Advances in computer vision-based civil infrastructure inspection and monitoring[J]. Engineering, 2019, 5(2):199-222.
[2] CHELLAPPA R. The changing fortunes of pattern recognition and computer vision[J]. Image and vision computing, 2016, 55:3-5.
[3] 雷明. 机器学习与应用[M]. 北京:清华大学出版社, 2019:26-33.
[4] MARY N A B, DHARMA D. Coral reef image classification employing improved LDP for feature extraction[J]. Journal of visual communication and image representation, 2017, 49:225-242.
[5] YU Hua, YANG Jie. A direct LDA algorithm for high-dimensional data-with application to face recognition[J]. Pattern recognition, 2001, 34(10):2067-2070.
[6] 刘丽, 匡纲要. 图像纹理特征提取方法综述[J]. 中国图象图形学报, 2009, 14(4):622-635
LIU Li, KUANG Gangyao. Overview of image textural feature extraction methods[J]. Journal of image and graphics, 2009, 14(4):622-635
[7] 李磊, 董卓丽. 利用改进图割的彩色图像分割算法[J]. 武汉大学学报·信息科学版, 2014, 39(12):1504-1508
LI Lei, DONG Zhuoli. Color image segmentation using improved graph cuts[J]. Geomatics and Information Science of Wuhan University, 2014, 39(12):1504-1508
[8] 弓晓虹, 郑音飞, 秦佳乐, 等. 基于乘性梯度的医学超声图像边缘检测算法[J]. 浙江大学学报(工学版), 2014, 48(10):1871-1878
GONG Xiaohong, ZHENG Yinfei, QIN Jiale, et al. Multiplicative gradient based edge detection method for medical ultrasound image[J]. Journal of Zhejiang University (Engineering Science), 2014, 48(10):1871-1878
[9] 张桂梅, 张松, 储珺. 一种新的基于局部轮廓特征的目标检测方法[J]. 自动化学报, 2014, 40(10):2346-2355
ZHANG Guimei, ZHANG Song, CHU Jun. A new object detection algorithm using local contour features[J]. Acta automatica sinica, 2014, 40(10):2346-2355
[10] YE Qihong, XIANG Ming, CUI Zhendong. Fingerprint image enhancement algorithm based on two dimension EMD and Gabor filter[J]. Procedia engineering, 2012, 29:1840-1844.
[11] 张丽琼, 王炳和. 基于小波变换的脉象信号特征提取方法[J]. 数据采集与处理, 2004, 19(3):323-328
ZHANG Liqiong, WANG Binghe. Feature extraction methods for pulse signal based on wavelet transform[J]. Journal of data acquisition & processing, 2004, 19(3):323-328
[12] YAGHOOBI H, MANSOURI H, FARSANGI M A E, et al. Determining the fragmented rock size distribution using textural feature extraction of images[J]. Powder technology, 2019, 342:630-641.
[13] MESNIL G, BORDES A, WESTON J, et al. Learning semantic representations of objects and their parts[J]. Machine learning, 2014, 94(2):281-301.
[14] 胡越, 罗东阳, 花奎, 等. 关于深度学习的综述与讨论[J]. 智能系统学报, 2019, 14(1):1-19
HU Yue, LUO Dongyang, HUA Kui, et al. Overview on deep learning[J]. CAAI transactions on intelligent systems, 2019, 14(1):1-19
[15] SERMANET P, EIGEN D, ZHANG Xiang, et al. Overfeat:integrated recognition, localization and detection using convolutional networks[J]. Computer science, 2013.
[16] ZEILER M D, KRISHNAN D, TAYLOR G W, et al. Deconvolutional networks[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010:2528-2535.
[17] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6):84-90.
[18] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556, 2014.
[19] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:770-778.
[20] HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:2261-2269.
[21] 郑远攀, 李广阳, 李晔. 深度学习在图像识别中的应用研究综述[J]. 计算机工程与应用, 2019, 55(12):20-36
ZHENG Yuanpan, LI Guangyang, LI Ye. Survey of application of deep learning in image recognition[J]. Computer engineering and applications, 2019, 55(12):20-36
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