摘要
近年来,细粒度图像识别逐渐成为计算机视觉领域的研究热点.由于不同类别图像间的视觉差异小、语义鸿沟问题严重,传统的基于视觉特征的细粒度图像识别性能往往不尽人意.针对这些挑战,目前许多学者都在研究基于用户点击数据的图像识别.本文围绕点击数据在图像识别中数据预处理、特征提取和模型构建3大模块中的应用,总结了已有的基于点击数据的识别算法及最新的研究进展.
In recent years,fine-grained image recognition has become a hotspot in computer vision area.Due to the subtle visual differences among different image categories and the serious semantic gap,the performance of traditional image recognition algorithms for fine-grained images recognition is mostly unsatisfactory.To overcome these challenges,many researchers have been concentrating on image recognition with user click data. This paper focuses on the three key modules of the fine-grained recognition system with user click data： data pre-processing,feature extracting and model construction.Also,existing algorithms for click data based image recognition are summarized,and the related latest progresses are demonstrated.
作者
俞俊
谭敏
张宏源
张海超
YU Jun;TAN Min;ZHANG Hongyuan;ZHANG Haichao(School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018;Key Laboratory of Complex Systems Modeling and Simulat ion,Hangzhou Dianzi University,Hangzhou 310018)
出处
《南京信息工程大学学报（自然科学版）》
CAS
2017年第6期567-574,共8页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
国家自然科学基金优秀青年基金(61622205)
国家自然科学基金青年基金(61602136)
关键词
用户点击
图像识别
度量学习
深度学习
语义鸿沟
user click data
image recognition
metric learning
deep learning
semantic gap
