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- Zhenguo Li, Xiao-Ming Wu, Shih-Fu Chang
- 2012 IEEE Conference on Computer Vision and…
- 2012

Grouping cues can affect the performance of segmentation greatly. In this paper, we show that superpixels (image segments) can provide powerful grouping cues to guide segmentation, where superpixels can be collected easily by (over)-segmenting the image using any reasonable existing segmentation algorithms. Generated by different algorithms with varying… (More)

- Zhenguo Li, Jianzhuang Liu, Xiaoou Tang
- ICML
- 2008

We consider the general problem of learning from both pairwise constraints and unlabeled data. The pairwise constraints specify whether two objects belong to the same class or not, known as the must-link constraints and the cannot-link constraints. We propose to learn a mapping that is smooth over the data graph and maps the data onto a unit hypersphere,… (More)

- Zhenguo Li, Jianzhuang Liu, Xiaoou Tang
- 2009 IEEE Conference on Computer Vision and…
- 2009

We propose a novel framework for constrained spectral clustering with pairwise constraints which specify whether two objects belong to the same cluster or not. Unlike previous methods that modify the similarity matrix with pairwise constraints, we adapt the spectral embedding towards an ideal embedding as consistent with the pairwise constraints as… (More)

- Zhenguo Li, Jianzhuang Liu
- 2009 IEEE 12th International Conference on…
- 2009

Clustering performance can often be greatly improved by leveraging side information. In this paper, we consider constrained clustering with pairwise constraints, which specify some pairs of objects from the same cluster or not. The main idea is to design a kernel to respect both the proximity structure of the data and the given pairwise constraints. We… (More)

Kernel learning is a powerful framework for nonlinear data modeling. Using the kernel trick, a number of problems have been formulated as semidefinite programs (SDPs). These include Maximum Variance Unfolding (MVU) (Weinberger et al., 2004) in nonlinear dimensionality reduction, and Pairwise Constraint Propagation (PCP) (Li et al., 2008) in constrained… (More)

- Xiao-Ming Wu, Zhenguo Li, Anthony Man-Cho So, John Wright, Shih-Fu Chang
- NIPS
- 2012

We propose a novel stochastic process that is with probability αi being absorbed at current state i, and with probability 1 − αi follows a random edge out of it. We analyze its properties and show its potential for exploring graph structures. We prove that under proper absorption rates, a random walk starting from a set S of low conductance will be mostly… (More)

- Zhenguo Li, Jianzhuang Liu, Shifeng Chen, Xiaoou Tang
- 2007 IEEE 11th International Conference on…
- 2007

This paper aims to introduce the robustness against noise into the spectral clustering algorithm. First, we propose a warping model to map the data into a new space on the basis of regularization. During the warping, each point spreads smoothly its spatial information to other points. After the warping, empirical studies show that the clusters become… (More)

- Jiefeng Cheng, Qin Liu, Zhenguo Li, Wei Fan, John C. S. Lui, Cheng He
- 2015 IEEE 31st International Conference on Data…
- 2015

Recent studies show that disk-based graph computation on just a single PC can be as highly competitive as cluster-based computing systems on large-scale problems. Inspired by this remarkable progress, we develop VENUS, a disk-based graph computation system which is able to handle billion-scale problems efficiently on a commodity PC. VENUS adopts a novel… (More)

- Go Irie, Zhenguo Li, Xiao-Ming Wu, Shih-Fu Chang
- 2014 IEEE Conference on Computer Vision and…
- 2014

Previous efforts in hashing intend to preserve data variance or pairwise affinity, but neither is adequate in capturing the manifold structures hidden in most visual data. In this paper, we tackle this problem by reconstructing the locally linear structures of manifolds in the binary Hamming space, which can be learned by locality-sensitive sparse coding.… (More)

- Liangliang Cao, Zhenguo Li, Yadong Mu, Shih-Fu Chang
- ACM Multimedia
- 2012

This paper develops a novel framework for efficient large-scale video retrieval. We aim to find video according to higher level similarities, which is beyond the scope of traditional near duplicate search. Following the popular hashing technique we employ compact binary codes to facilitate nearest neighbor search. Unlike the previous methods which… (More)