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Stan Li

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4

AAAI Conference 2017 Conference Paper

Unsupervised Learning of Multi-Level Descriptors for Person Re-Identification

  • Yang Yang
  • Longyin Wen
  • Siwei Lyu
  • Stan Li

In this paper, we propose a novel coding method named weighted linear coding (WLC) to learn multi-level (e. g. , pixel-level, patch-level and image-level) descriptors from raw pixel data in an unsupervised manner. It guarantees the property of saliency with a similarity constraint. The resulting multi-level descriptors have a good balance between the robustness and distinctiveness. Based on WLC, all data from the same region can be jointly encoded. Consequently, when we extract the holistic image features, it is able to preserve the spatial consistency. Furthermore, we apply PCA to these features and compact person representations are then achieved. During the stage of matching persons, we exploit the complementary information resided in multi-level descriptors via a score-level fusion strategy. Experiments on the challenging person re-identification datasets - VIPeR and CUHK 01, demonstrate the effectiveness of our method.

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AAAI Conference 2016 Conference Paper

Large Scale Similarity Learning Using Similar Pairs for Person Verification

  • Yang Yang
  • Shengcai Liao
  • Zhen Lei
  • Stan Li

In this paper, we propose a novel similarity measure and then introduce an efficient strategy to learn it by using only similar pairs for person verification. Unlike existing metric learning methods, we consider both the difference and commonness of an image pair to increase its discriminativeness. Under a pairconstrained Gaussian assumption, we show how to obtain the Gaussian priors (i. e. , corresponding covariance matrices) of dissimilar pairs from those of similar pairs. The application of a log likelihood ratio makes the learning process simple and fast and thus scalable to large datasets. Additionally, our method is able to handle heterogeneous data well. Results on the challenging datasets of face verification (LFW and Pub- Fig) and person re-identification (VIPeR) show that our algorithm outperforms the state-of-the-art methods.

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AAAI Conference 2016 Conference Paper

Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation

  • Yang Yang
  • Zhen Lei
  • Shifeng Zhang
  • Hailin Shi
  • Stan Li

A variety of encoding methods for bag of word (BoW) model have been proposed to encode the local features in image classification. However, most of them are unsupervised and just employ k-means to form the visual vocabulary, thus reducing the discriminative power of the features. In this paper, we propose a metric embedded discriminative vocabulary learning for high-level person representation with application to person re-identification. A new and effective term is introduced which aims at making the same persons closer while different ones farther in the metric space. With the learned vocabulary, we utilize a linear coding method to encode the imagelevel features (or holistic image features) for extracting highlevel person representation. Different from traditional unsupervised approaches, our method can explore the relationship (same or not) among the persons. Since there is an analytic solution to the linear coding, it is easy to obtain the final high-level features. The experimental results on person reidentification demonstrate the effectiveness of our proposed algorithm.

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NeurIPS Conference 2002 Conference Paper

FloatBoost Learning for Classification

  • Stan Li
  • Zhenqiu Zhang
  • Heung-Yeung Shum
  • Hongjiang Zhang

AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the training set [14]. However, the ultimate goal in applications of pattern classification is always minimum error rate. On the other hand, AdaBoost needs an effective procedure for learning weak classifiers, which by itself is difficult especially for high dimensional data. In this paper, we present a novel procedure, called FloatBoost, for learning a better boosted classifier. FloatBoost uses a backtrack mechanism after each iteration of AdaBoost to remove weak classifiers which cause higher error rates. The resulting float-boosted classifier consists of fewer weak classifiers yet achieves lower error rates than AdaBoost in both training and test. We also propose a statistical model for learning weak classifiers, based on a stagewise approximation of the posterior using an overcomplete set of scalar features. Experi- mental comparisons of FloatBoost and AdaBoost are provided through a difficult classification problem, face detection, where the goal is to learn from training examples a highly nonlinear classifier to differentiate be- tween face and nonface patterns in a high dimensional space. The results clearly demonstrate the promises made by FloatBoost over AdaBoost.

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