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Xiaoke Zhu

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4 papers

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IJCAI Conference 2017 Conference Paper

Discriminant Tensor Dictionary Learning with Neighbor Uncorrelation for Image Set Based Classification

Fei Wu
Xiao-Yuan Jing
Wangmeng Zuo
Ruiping Wang
Xiaoke Zhu

Image set based classification (ISC) has attracted lots of research interest in recent years. Several ISC methods have been developed, and dictionary learning technique based methods obtain state-of-the-art performance. However, existing ISC methods usually transform the image sample of a set into a vector for subsequent processing, which breaks the inherent spatial structure of image sample and the set. In this paper, we utilize tensor to model an image set with two spatial modes and one set mode, which can fully explore the intrinsic structure of image set. We propose a novel ISC approach, named discriminant tensor dictionary learning with neighbor uncorrelation (DTDLNU), which jointly learns two spatial dictionaries and one set dictionary. The spatial and set dictionaries are composed by set-specific sub-dictionaries corresponding to the class labels, such that the reconstruction error is discriminative. To obtain dictionaries with favorable discriminative power, DTDLNU designs a neighbor-uncorrelated discriminant tensor dictionary term, which minimizes the within-class scatter of the training sets in the projected tensor space and reduces tensor dictionary correlation among set-specific sub-dictionaries corresponding to neighbor sets from different classes. Experiments on three challenging datasets demonstrate the effectiveness of DTDLNU.

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AAAI Conference 2017 Conference Paper

Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image

Xiaoke Zhu
Xiao-Yuan Jing
Fei Wu
Yunhong Wang
Wangmeng Zuo
Wei-Shi Zheng

Person re-identiﬁcation (re-id) plays an important role in video surveillance and forensics applications. In many cases, person re-id needs to be conducted between image and video clip, e. g. , re-identifying a suspect from large quantities of pedestrian videos given a single image of him. We call reid in this scenario as image to video person re-id (IVPR). In practice, image and video are usually represented with different features, and there usually exist large variations between frames within each video. These factors make matching between image and video become a very challenging task. In this paper, we propose a joint feature projection matrix and heterogeneous dictionary pair learning (PHDL) approach for IVPR. Speciﬁcally, PHDL jointly learns an intra-video projection matrix and a pair of heterogeneous image and video dictionaries. With the learned projection matrix, the inﬂuence of variations within each video to the matching can be reduced. With the learned dictionary pair, the heterogeneous image and video features can be transformed into coding coefﬁcients with the same dimension, such that the matching can be conducted using coding coefﬁcients. Furthermore, to ensure that the obtained coding coefﬁcients have favorable discriminability, PHDL designs a point-to-set coefﬁcient discriminant term. Experiments on the public iLIDS-VID and PRID 2011 datasets demonstrate the effectiveness of the proposed approach.

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AAAI Conference 2017 Conference Paper

Multi-Kernel Low-Rank Dictionary Pair Learning for Multiple Features Based Image Classification

Xiaoke Zhu
Xiao-Yuan Jing
Fei Wu
Di Wu
Li Cheng
Sen Li
Ruimin Hu

Dictionary learning (DL) is an effective feature learning technique, and has led to interesting results in many classiﬁcation tasks. Recently, by combining DL with multiple kernel learning (which is a crucial and effective technique for combining different feature representation information), a few multi-kernel DL methods have been presented to solve the multiple feature representations based classiﬁcation problem. However, how to improve the representation capability and discriminability of multi-kernel dictionary has not been well studied. In this paper, we propose a novel multi-kernel DL approach, named multi-kernel low-rank dictionary pair learning (MKLDPL). Speciﬁcally, MKLDPL jointly learns a kernel synthesis dictionary and a kernel analysis dictionary by exploiting the class label information. The learned synthesis and analysis dictionaries work together to implement the coding and reconstruction of samples in the kernel space. To enhance the discriminability of the learned multi-kernel dictionaries, MKLDPL imposes the low-rank regularization on the analysis dictionary, which can make samples from the same class have similar representations. We apply MKLDPL for multiple features based image classiﬁcation task. Experimental results demonstrate the effectiveness of the proposed approach.

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IJCAI Conference 2016 Conference Paper

Video-Based Person Re-Identification by Simultaneously Learning Intra-Video and Inter-Video Distance Metrics

Xiaoke Zhu
Xiao-Yuan Jing
Fei Wu
Hui Feng

Video-based person re-identification (re-id) is an important application in practice. However, only a few methods have been presented for this problem. Since large variations exist between different pedestrian videos, as well as within each video, it's challenging to conduct re-identification between pedestrian videos. In this paper, we propose a simultaneous intra-video and inter-video distance learning (SI2DL) approach for video-based person re-id. Specifically, SI2DL simultaneously learns an intra-video distance metric and an inter-video distance metric from the training videos. The intra-video distance metric is to make each video more compact, and the inter-video one is to make that the distance between two truly matching videos is smaller than that between two wrong matching videos. To enhance the discriminability of learned metrics, we design a video relationship model, i. e. , video triplet, for SI2DL. Experiments on the public iLIDS-VID and PRID 2011 image sequence datasets show that our approach achieves the state-of-the-art performance.

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