7,937 research outputs found
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
Person re-identification is an important technique towards automatic search
of a person's presence in a surveillance video. Two fundamental problems are
critical for person re-identification, feature representation and metric
learning. An effective feature representation should be robust to illumination
and viewpoint changes, and a discriminant metric should be learned to match
various person images. In this paper, we propose an effective feature
representation called Local Maximal Occurrence (LOMO), and a subspace and
metric learning method called Cross-view Quadratic Discriminant Analysis
(XQDA). The LOMO feature analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation against viewpoint
changes. Besides, to handle illumination variations, we apply the Retinex
transform and a scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional subspace by
cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is
learned on the derived subspace. We also present a practical computation method
for XQDA, as well as its regularization. Experiments on four challenging person
re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show
that the proposed method improves the state-of-the-art rank-1 identification
rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and
extracted features please visit
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda
A Family of Maximum Margin Criterion for Adaptive Learning
In recent years, pattern analysis plays an important role in data mining and
recognition, and many variants have been proposed to handle complicated
scenarios. In the literature, it has been quite familiar with high
dimensionality of data samples, but either such characteristics or large data
have become usual sense in real-world applications. In this work, an improved
maximum margin criterion (MMC) method is introduced firstly. With the new
definition of MMC, several variants of MMC, including random MMC, layered MMC,
2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the
MMC network is developed to learn deep features of images in light of simple
deep networks. Experimental results on a diversity of data sets demonstrate the
discriminant ability of proposed MMC methods are compenent to be adopted in
complicated application scenarios.Comment: 14 page
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