J
Jie Yang
Researcher at Shanghai Jiao Tong University
Publications - 680
Citations - 12772
Jie Yang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Image segmentation & Feature extraction. The author has an hindex of 46, co-authored 629 publications receiving 10558 citations. Previous affiliations of Jie Yang include East China University of Science and Technology & Chinese Ministry of Education.
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Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
TL;DR: Wang et al. as mentioned in this paper proposed a multi-stage framework for suture thread detection based on deep learning, which fused the original image to learn a gradient road map of the thread.
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Adaptive Initialization Method for K-means Algorithm
TL;DR: This research proposes an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results and significantly reduce the time complexity.
Journal ArticleDOI
Unsupervised Change Detection of SAR Images Based on an Improved NSST Algorithm
TL;DR: The proposed algorithm based on non-subsampled shearlet transform (NSST) detection in SAR images, for unsupervised changes has higher detection accuracy than the FLICM, DWT2-FLicM, and NSCT-FLICM algorithms.
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Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval.
TL;DR: A novel Asymmetric Correlation Quantization Hashing (ACQH) method, which learns the projection matrixs of heterogeneous modalities data points for transforming query into a low-dimensional real-valued vector in latent semantic space and constructs the stacked compositional quantization embedding in a coarse-to-fine manner.
Proceedings ArticleDOI
Image sentiment analysis using supervised collective matrix factorization
TL;DR: This paper provides a way to utilize the strengths of these techniques to develop a sophisticated method, called Supervised Collective Matrix Factorization (SCMF), which takes label information into consideration during matrix factorization, inspired by the graph Laplacian work.