activity
20102022
most citedIntrinsic dimension estimation of data by principal component analysis

23 citations · 41 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20221 cited

Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning

Lu Zhang, Lu Qi, Xu Yang +3

This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. The NCD task is challenging due to…

cs.LG20164 cited

Effective Deterministic Initialization for -Means-Like Methods via Local Density Peaks Searching

Fengfu Li, Hong Qiao, Bo Zhang

The -means clustering algorithm is popular but has the following main drawbacks: 1) the number of clusters, , needs to be provided by the user in advance, 2) it can easily re…

cs.CV20169 cited

Perceptual uniform descriptor and Ranking on manifold: A bridge between image representation and ranking for image retrieval

Shenglan Liu, Jun Wu, Lin Feng +4

Incompatibility of image descriptor and ranking is always neglected in image retrieval. In this paper, manifold learning and Gestalt psychology theory are involved to solve the inc…

cs.CV2016

Poisson Noise Reduction with Higher-order Natural Image Prior Model

Wensen Feng, Hong Qiao, Yunjin Chen

Poisson denoising is an essential issue for various imaging applications, such as night vision, medical imaging and microscopy. State-of-the-art approaches are clearly dominated by…

cs.DS20144 cited

A Weighted Common Subgraph Matching Algorithm

Xu Yang, Hong Qiao, Zhi-Yong Liu

We propose a weighted common subgraph (WCS) matching algorithm to find the most similar subgraphs in two labeled weighted graphs. WCS matching, as a natural generalization of the e…

cs.CV201023 cited

Intrinsic dimension estimation of data by principal component analysis

Mingyu Fan, Nannan Gu, Hong Qiao +1

Estimating intrinsic dimensionality of data is a classic problem in pattern recognition and statistics. Principal Component Analysis (PCA) is a powerful tool in discovering dimensi…