计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 255-259.doi: 10.11896/jsjkx.200900033
陈扬1,2, 王金亮3, 夏炜3, 杨颢1,2, 朱润3, 奚雪峰1,2
CHEN Yang1,2, WANG Jin-liang3, XIA Wei3, YANG Hao1,2, ZHU Run3, XI Xue-feng1,2
摘要： 足迹图像是公安在串并案的侦破过程中最为重要的线索,且每年各处公安都会收集很多犯罪现场的足迹,如何自动化地整理和归类这些足迹图像成为当前公安信息化的一个难点。面向公安实战需求,文中结合卷积神经网络和DBSCAN算法,提出了一种对足迹图像聚类的方法。首先,对足迹图像进行预处理以便满足模型训练要求;接着,通过模型预训练改进了Resnnet50和Densenet121两类卷积神经网络模型结构,提取足迹图像特征并建立特征向量库;随后,基于DBSCAN聚类算法,利用上述特征向量库实现对足迹图像的整理归类。实验结果表明,该方法具有良好的实用性和有效性。
中图分类号:
| [1] DICKENSON M,GUEGUEN L.Rotated Rectangles for Symbolized Building Footprint Extraction[C]//IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition Workshops.2018:225-228. [2] SUN Y,WANG Y Y,ZHU X X.Automatic Registration of SAR Image and GIS Building Footprints Data in Dense Urban Area[C]//2019 IEEE International Geoscience and Remote Sensing Symposium.2019:927-930. [3] LI Z S.Simulation Study on the Rapid Identification of Footprint Image of Aerobics Athletes[J].Computer Simulation,2017,34(3):221-224. [4] CHEN Y,ZENG C,CHENG C,et al.A CNN-based Approach to Footprint Image Retrieval and Matching[J].Journal of NanJing Normal University(Engineering and Technology Edition),2018,18(3):45-51. [5] ZHOU F Y,JIN L P,DONG J.Review of Convolutional Neural Network[J].Chinese Journal of Computers,2017,40(6):1229-1251. [6] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [7] CHEN S D,WEI W,HE B Q,et al.Action recognition base on improved deep convolutional neural network[J].Application Research of Computers,2019(3):62. [8] HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [9] HUANG G,LIU Z,VAN DER MAATEN L,et al.Densely con-nected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4700-4708. [10] WANG J H,ADRIAN H,JIANG J M.Spectral Analysis Network for Deep Representation Learning and Image Clustering[C]//2019 IEEE International Conference on Multimedia and Expo (ICME).2019:1540-1545. [11] SCHUBERT E,SANDER J,ESTER M,et al.DBSCAN revisited,revisited:why and how you should (still) use DBSCAN[J].ACM Transactions on Database Systems (TODS),2017,42(3):1-21. [12] NEWMAN M E J,CANTWELL G T,YOUNG J G.Improved mutual information measure for clustering,classification,and community detection[J].Physical Review E,2020,101(4):23-34. [13] SUN S L,WANG C,ZHAO Y D.Parameter independent clustering of air traffic trajectory based on silhouette coefficient[J].Journal of Computer Applications,2019,39(11):3293-3297. [14] QIAN X D,LUO Y F.Incomplete Data Clustering Algorithm Based on Mutual Information Attributes Ranking[J].Information and Control,2019,48(1):84-91. [15] XIE J Y,ZHOU Y,WANG M Z,et al.CAAI Transactions on Intelligent Systems[J].New Criteria for Evaluating the Validity of Clustering,2017,12(6):873-882. |
| [1] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
| [2] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
| [3] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
| [4] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
| [5] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
| [6] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
| [7] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
| [8] | 刘月红, 牛少华, 神显豪. 基于卷积神经网络的虚拟现实视频帧内预测编码 Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network 计算机科学, 2022, 49(7): 127-131. https://doi.org/10.11896/jsjkx.211100179 |
| [9] | 徐鸣珂, 张帆. Head Fusion:一种提高语音情绪识别的准确性和鲁棒性的方法 Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition 计算机科学, 2022, 49(7): 132-141. https://doi.org/10.11896/jsjkx.210100085 |
| [10] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
| [11] | 杨玥, 冯涛, 梁虹, 杨扬. 融合交叉注意力机制的图像任意风格迁移 Image Arbitrary Style Transfer via Criss-cross Attention 计算机科学, 2022, 49(6A): 345-352. https://doi.org/10.11896/jsjkx.210700236 |
| [12] | 杨健楠, 张帆. 一种结合双注意力机制和层次网络结构的细碎农作物分类方法 Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure 计算机科学, 2022, 49(6A): 353-357. https://doi.org/10.11896/jsjkx.210200169 |
| [13] | 孙福权, 崔志清, 邹彭, 张琨. 基于多尺度特征的脑肿瘤分割算法 Brain Tumor Segmentation Algorithm Based on Multi-scale Features 计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217 |
| [14] | 吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039 |
| [15] | 杨涵, 万游, 蔡洁萱, 方铭宇, 吴卓超, 金扬, 钱伟行. 基于步态分类辅助的虚拟IMU的行人导航方法 Pedestrian Navigation Method Based on Virtual Inertial Measurement Unit Assisted by GaitClassification 计算机科学, 2022, 49(6A): 759-763. https://doi.org/10.11896/jsjkx.211200148 |
|
||
首页