Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition

We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images. We use feature restructuring to exploit the correlations of both inner$\&$inter-set images. Specifically, the residual self-attention can effectively restructure the features using the other features within a set to emphasize the discriminative images and eliminate the redundancy. Then, a sparse/collaborative learning-based dependency-guided representation scheme reconstructs the probe features conditional to the gallery features in order to adaptively align the two sets. This enables our framework to be compatible with both verification and open-set identification. We show that the parametric self-attention network and non-parametric dictionary learning can be trained end-to-end by a unified alternative optimization scheme, and that the full framework is permutation-invariant. In the numerical experiments we conducted, our method achieves top performance on competitive image set/video-based face recognition and person re-identification benchmarks.Comment: Accepted to ICCV 201

Breaking permutation-based mesh steganography and security improvement

Permutation-based steganography in polygonal meshes can provide considerably large embedding capacities for hiding secret messages. However, corresponding steganalysis techniques against such methods have never been studied. This paper identifies the essential differences between naturally generated meshes and meshes produced by permutation-based steganography methods. It is found that the two types of meshes differ significantly in the distribution of topological distances between consecutive mesh elements. Therefore, by measuring the orderliness of the vertex list and the face list of meshes, we develop solutions for several mesh steganalysis problems. These solutions are effective, leading to high detection accuracy; and they are also universal, requiring no knowledge such as which steganography method is used and what data embedding rate is adopted for the detection mechanism to work. Moreover, this paper also presents a security-improved permutation-based mesh steganography approach, by taking advantage of the connectivity information of polygonal meshes and establishing a good trade-off between embedding capacity and undetectability. Without bringing global changes, our approach embeds secret messages into local neighborhoods on meshes. As a result, meshes generated by the proposed steganography approach tend to have natural structures that are unlikely to draw suspicions to steganalyzers.Ministry of Education (MOE)Published versionThe work was supported by the National Natural Science Foundation of China under Grant 61402279, Grant U1736213, and Grant 61572308, and by the Singapore MOE Tier-2 Grants under 2016-T2-2-065 and 2017-T2-1-076

A joint optimization framework of low-dimensional projection and collaborative representation for discriminative classification

Abstract Various representation-based methods have been developed and shown great potential for pattern classification. To further improve their discriminability, we propose a Bi-level optimization framework in terms of both low-dimensional projection and collaborative representation. Specifically, during the projection phase, we try to minimize the intra-class similarity and inter-class dissimilarity, while in the representation phase, our goal is to achieve the lowest correlation of the representation results. Solving this joint optimization mutually reinforces both aspects of feature projection and representation. Experiments on face recognition, object categorization and scene classification dataset demonstrate remarkable performance improvements led by the proposed framework

Tissue-Based Proteogenomics Reveals that Human Testis Endows Plentiful Missing Proteins

Investigations of missing proteins (MPs) are being endorsed by many bioanalytical strategies. We proposed that proteogenomics of testis tissue was a feasible approach to identify more MPs because testis tissues have higher gene expression levels. Here we combined proteomics and transcriptomics to survey gene expression in human testis tissues from three post-mortem individuals. Proteins were extracted and separated with glycine- and tricine-SDS-PAGE. A total of 9597 protein groups were identified; of these, 166 protein groups were listed as MPs, including 138 groups (83.1%) with transcriptional evidence. A total of 2948 proteins are designated as MPs, and 5.6% of these were identified in this study. The high incidence of MPs in testis tissue indicates that this is a rich resource for MPs. Functional category analysis revealed that the biological processes that testis MPs are mainly involved in are sexual reproduction and spermatogenesis. Some of the MPs are potentially involved in tumorgenesis in other tissues. Therefore, this proteogenomics analysis of individual testis tissues provides convincing evidence of the discovery of MPs. All mass spectrometry data from this study have been deposited in the ProteomeXchange (data set identifier PXD002179)