Please wait a minute...
Computer & Telecommunication  2018, Vol. 1 Issue (11): 78-80    DOI:
Current Issue | Archive | Adv Search |
Fine Grained Image Localization Based on Bilinear Convolutional Neural Network
First High School of Fu’an City
Download:   PDF(0KB)
Export: BibTeX | EndNote (RIS)      
Abstract  Fine-grained Image recognition is a difficult problem for computer vision area, and it is also a challenge for big data and artificial intelligence. In order to solve this problem, an object localization method based on bilinear convolutional network is proposed. The bilinear convolutional network is used to obtain different features of VGG network, and the intersection set is used as the localization framework. In order to verify the feasibility of this paper, the proposed method is simulated on CUB-200-2011 and Stanford Cars-196. The simulation result shows the accuracy of the proposed method is higher than other representative methods. After adding the Softmax classier, this proposed method improved largely compared with the primitive image as the input.
Key wordsfine-grained recognition      object localization      feature      bilinear network     
Published: 16 January 2019
:  TP393  

Cite this article:

CHEN Si-qi. Fine Grained Image Localization Based on Bilinear Convolutional Neural Network . Computer & Telecommunication, 2018, 1(11): 78-80.

URL:

https://www.computertelecom.com.cn/EN/     OR     https://www.computertelecom.com.cn/EN/Y2018/V1/I11/78

[1] ZENG Hao,  YANG Cheng, . An Algorithm for Identifying X-ray Pulsar Signals[J]. 电脑与电信, 2024, 1(5): 6-.
[2] REN Hao. Expression Recognition Method Combined with Attention Feature Fusion[J]. 电脑与电信, 2024, 1(5): 71-.
[3] WANG Jin WANG Rui. Facial Expression Recognition Algorithm Based on Multi-scale Feature Deep Learning[J]. 电脑与电信, 2024, 1(5): 75-.
[4] ZHANG Li.
Research on Macro Virus Processing Model Based on SVM Algorithm
[J]. 电脑与电信, 2022, 1(1-2): 41-45.
[5] ZHANG Miao-miao CHAI Guo-qiang YU Hai-le XU Hao-xuan.
Facial Feature Test and Fatigue Driving Warning Based on Deep Learning
[J]. 电脑与电信, 2022, 1(12): 1-.
[6] LIU Hui-fang. Research and Application of Anti-fraud Model Based on Big Data of Communication Operator[J]. 电脑与电信, 2021, 1(7): 46-52.
[7] SHEN Ya-ting SHAO Ying BIAN Kai.
Summary of Research on Book Classification Model Technology in Smart Library
[J]. 电脑与电信, 2021, 1(12): 9-13.
[8] WANG Zhuang-zhuang LI Zhu. Improved Sobel Edge Detection Operator Based on Butterworth Filter[J]. 电脑与电信, 2020, 1(8): 19-22.
[9] HUANG Jing YANG Shu-guo LIU Zi-zheng. AMethod for Image Retrieval with Capsule Network[J]. 电脑与电信, 2020, 1(6): 52-56.
[10] NING Chen ZHANG Li-he WANG Xin . Multi-sensor Image Matching Method Based on Joint Graph Spectral FeatureAnalysis[J]. 电脑与电信, 2020, 1(12): 1-3.
[11] ZHU Yuan-zhi LU Ru-hua LI Jia LI Ya-lan . [J]. 电脑与电信, 2020, 1(10): 9-12.
[12] ZHANG Hui-fang ZONG Cai-le ZHANG Xiao-lin. Chinese Text Feature Classification Based on Distributed Framework[J]. 电脑与电信, 2019, 1(5): 1-7.
[13] TONG Lian. Research and Application of Machine Learning in Big Data[J]. 电脑与电信, 2018, 1(9): 29-31.
[14] . Study on Bidirectional Feature Matching Algorithm Based on Standardized Euclidean Distance[J]. 电脑与电信, 2018, 1(11): 35-40.
[15] Shi Xueying. Study on Feature Representation in Handwritten Numeral Recognition Based on Autoencoders[J]. 电脑与电信, 2017, 1(1-2): 38-39.
Copyright © Computer & Telecommunication, All Rights Reserved.
Powered by Beijing Magtech Co. Ltd