Unsupervised deep hashing with node representation for image retrieval
Authors:
- Yangtao WangYWYangtao Wang
- Jingkuan SongJSJingkuan Song
- Ke ZhouKZKe Zhou
- Yu LiuYLYu Liu
- 1 April 2021
- journal article
- research article
- Published by Elsevier in Pattern Recognition
- Vol. 112
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China
- Innovative Research Group Project of the National Natural Science Foundation of China
This publication has 18 references indexed in Scilit:
- Zero-shot Hashing with orthogonal projection for image retrievalPattern Recognition Letters, 2019
- Deep unsupervised learning of visual similaritiesPattern Recognition, 2018
- Deep Self-Taught Hashing for Image RetrievalIEEE Transactions on Cybernetics, 2018
- Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasksPattern Recognition, 2018
- Quantization-based hashing: a general framework for scalable image and video retrievalPattern Recognition, 2018
- Robust discrete code modeling for supervised hashingPattern Recognition, 2018
- Guest Editorial: Large-Scale Multimedia Data Retrieval, Classification, and UnderstandingIEEE Transactions on Multimedia, 2017
- Deep Learning of Graphs with Ngram Convolutional Neural NetworksIEEE Transactions on Knowledge and Data Engineering, 2017
- Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image RetrievalIEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
- Laplacian Eigenmaps for Dimensionality Reduction and Data RepresentationNeural Computation, 2003