摘要
为了提高跨媒体检索的效率,可行的方法是降低数据集中不相关内容的比例。采用局部敏感哈希算法将图像数据映射到汉明空间并利用神经网络学习将文本数据映射到汉明空间的哈希函数,提出一种可以显著提高数据集中相关文件比例的高效跨媒体检索方法。实验结果表明,提出的方法能够有效去除数据集中的不相关内容,相对于已有的跨媒体检索方法,其有效提高了检索效率与准确率。
To efficiently retrieve in multimodal data,it is essential to reduce the proportion of irrelevant documents.The image data were projected to the Hamming space by using the locality-sensitive hashing algorithm,the text data were mapped on the hashing function of Hamming space by employing the neural network learning,and then a novel cross-media retrieval approach was proposed to reduce the proportion of irrelevant documents.The experiment shows that the proportion of the relevant documents can be much improved in the proposed method.Assessments on the two public datasets also demonstrate the efficacy and the accuracy of the proposed retrieval method when compared to the baselines.
作者
白亮
贾玉华
王昊冉
谢毓湘
于天元
BAI Liang;JIA Yuhua;ANG Haoran;XIE Yuxiang;YU Tianyuan(College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China)
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2018年第1期93-98,共6页
Journal of National University of Defense Technology
基金
国家自然科学基金资助项目(61571453)
湖南省自然科学基金资助项目(14JJ3010)
关键词
跨媒体检索
神经网络算法
局部敏感哈希算法
多模态数据索引
cross-media retrieval
neural network algorithm
locality-sensitive hashing algorithm
multimodal data indexing
