arXiv subjects

Ying Ji

Publications and source records attributed to Ying Ji.

8 recordsLinked to original sources

arXiv2025

Hybrid second-order gradient histogram based global low-rank sparse regression for robust face recognition

Low-rank sparse regression models have been widely adopted in face recognition due to their robustness against occlusion and illumination variations. However, existing methods often suffer from insufficient feature representation and limited modeling of structured corruption across samples. To address these issues, this paper proposes a Hybrid second-order gradient Histogram based Global Low-Rank Sparse Regression (H2H-GLRSR) model. First, we propose the Histogram of Oriented Hessian (HOH) to capture second-order geometric characteristics such as curvature and ridge patterns. By fusing HOH and first-order gradient histograms, we construct a unified local descriptor, termed the Hybrid second-order gradient Histogram (H2H), which enhances structural discriminability under challenging conditions. Subsequently, the H2H features are incorporated into an extended version of the Sparse Regularized Nuclear Norm based Matrix Regression (SR\_NMR) model, where a global low-rank constraint is imposed on the residual matrix to exploit cross-sample correlations in structured noise. The resulting H2H-GLRSR model achieves superior discrimination and robustness. Experimental results on benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art regression-based classifiers in both recognition accuracy and computational efficiency.

cs.CV↗

arXiv2024

A Semiparametric Gaussian Mixture Model for Chest CT-based 3D Blood Vessel Reconstruction

Computed tomography (CT) has been a powerful diagnostic tool since its emergence in the 1970s. Using CT data, three-dimensional (3D) structures of human internal organs and tissues, such as blood vessels, can be reconstructed using professional software. This 3D reconstruction is crucial for surgical operations and can serve as a vivid medical teaching example. However, traditional 3D reconstruction heavily relies on manual operations, which are time-consuming, subjective, and require substantial experience. To address this problem, we develop a novel semiparametric Gaussian mixture model tailored for the 3D reconstruction of blood vessels. This model extends the classical Gaussian mixture model by enabling nonparametric variations in the component-wise parameters of interest according to voxel positions. We develop a kernel-based expectation-maximization algorithm for estimating the model parameters, accompanied by a supporting asymptotic theory. Furthermore, we propose a novel regression method for optimal bandwidth selection. Compared to the conventional cross-validation-based (CV) method, the regression method outperforms the CV method in terms of computational and statistical efficiency. In application, this methodology facilitates the fully automated reconstruction of 3D blood vessel structures with remarkable accuracy.

stat.AP↗

arXiv2024

SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image Classification

To address the issues of MixUp and its variants (e.g., Manifold MixUp) in image classification tasks-namely, their neglect of mixing within the same class (intra-class mixup) and their inadequacy in enhancing intra-class cohesion through their mixing operations-we propose a novel mixup method named SynerMix-Intra and, building upon this, introduce a synergistic mixup solution named SynerMix. SynerMix-Intra specifically targets intra-class mixup to bolster intra-class cohesion, a feature not addressed by current mixup methods. For each mini-batch, it leverages feature representations of unaugmented original images from each class to generate a synthesized feature representation through random linear interpolation. All synthesized representations are then fed into the classification and loss layers to calculate an average classification loss that significantly enhances intra-class cohesion. Furthermore, SynerMix combines SynerMix-Intra with an existing mixup approach (e.g., MixUp, Manifold MixUp), which primarily focuses on inter-class mixup and has the benefit of enhancing inter-class separability. In doing so, it integrates both inter- and intra-class mixup in a balanced way while concurrently improving intra-class cohesion and inter-class separability. Experimental results on six datasets show that SynerMix achieves a 0.1% to 3.43% higher accuracy than the best of either MixUp or SynerMix-Intra alone, averaging a 1.16% gain. It also surpasses the top-performer of either Manifold MixUp or SynerMix-Intra by 0.12% to 5.16%, with an average gain of 1.11%. Given that SynerMix is model-agnostic, it holds significant potential for application in other domains where mixup methods have shown promise, such as speech and text classification. Our code is publicly available at: https://github.com/wxitxy/synermix.git.

cs.CV↗

arXiv2024

Entanglement harvesting in cosmic string spacetime

We investigate the entanglement harvesting phenomenon for static detectors that locally interact with massless scalar fields in the cosmic string spacetime, which, though locally flat, features a conical structure defined by a deficit angle. Specifically, we analyze three detector alignments relative to the string: parallel and orthogonal alignments with detectors on the same side of the string, and an orthogonal alignment with detectors on opposite sides of the string. For the alignments on the same side of the string, we observe that the cosmic string's presence can either aid or hinder entanglement harvesting, affecting both the extent of entanglement harvested and the achievable range of interdetector separation. This effect depends on the distance between the detectors and the string and differs markedly from scenarios in a locally flat spacetime with a reflecting boundary, where the boundary invariably extends the harvesting-achievable range. Conversely, for the alignment with detectors on opposite sides of the string, we find that detectors consistently harvest more entanglement than those in a flat spacetime devoid of a cosmic string. This starkly contrasts the behavior observed with detectors on the same side. Interestingly, the presence of a cosmic string expands the harvesting-achievable range for detectors in orthogonal alignment only when near the string, whereas it invariably reduces the achievable range for detectors in parallel alignment.

quant-ph↗

arXiv2023

Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma Subtype Recognition

The most common type of lung cancer, lung adenocarcinoma (LUAD), has been increasingly detected since the advent of low-dose computed tomography screening technology. In clinical practice, pre-invasive LUAD (Pre-IAs) should only require regular follow-up care, while invasive LUAD (IAs) should receive immediate treatment with appropriate lung cancer resection, based on the cancer subtype. However, prior research on diagnosing LUAD has mainly focused on classifying Pre-IAs/IAs, as techniques for distinguishing different subtypes of IAs have been lacking. In this study, we proposed a multi-head attentional feature fusion (MHA-FF) model for not only distinguishing IAs from Pre-IAs, but also for distinguishing the different subtypes of IAs. To predict the subtype of each nodule accurately, we leveraged both radiomics and deep features extracted from computed tomography images. Furthermore, those features were aggregated through an adaptive fusion module that can learn attention-based discriminative features. The utility of our proposed method is demonstrated here by means of real-world data collected from a multi-center cohort.

eess.IV↗

arXiv2023

High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach

Lung cancer, particularly in its advanced stages, remains a leading cause of death globally. Though early detection via low-dose computed tomography (CT) is promising, the identification of high-risk factors crucial for surgical mode selection remains a challenge. Addressing this, our study introduces an Attention-Enhanced Graph Convolutional Network (AE-GCN) model to classify whether there are high-risk factors in stage I lung cancer based on the preoperative CT images. This will aid surgeons in determining the optimal surgical method before the operation. Unlike previous studies that relied on 3D patch techniques to represent nodule spatial features, our method employs a GCN model to capture the spatial characteristics of pulmonary nodules. Specifically, we regard each slice of the nodule as a graph vertex, and the inherent spatial relationships between slices form the edges. Then, to enhance the expression of nodule features, we integrated both channel and spatial attention mechanisms with a pre-trained VGG model for adaptive feature extraction from pulmonary nodules. Lastly, the effectiveness of the proposed method is demonstrated using real-world data collected from the hospitals, thereby emphasizing its potential utility in the clinical practice.

eess.IV↗

arXiv2022

One-shot Network Pruning at Initialization with Discriminative Image Patches

One-shot Network Pruning at Initialization (OPaI) is an effective method to decrease network pruning costs. Recently, there is a growing belief that data is unnecessary in OPaI. However, we obtain an opposite conclusion by ablation experiments in two representative OPaI methods, SNIP and GraSP. Specifically, we find that informative data is crucial to enhancing pruning performance. In this paper, we propose two novel methods, Discriminative One-shot Network Pruning (DOP) and Super Stitching, to prune the network by high-level visual discriminative image patches. Our contributions are as follows. (1) Extensive experiments reveal that OPaI is data-dependent. (2) Super Stitching performs significantly better than the original OPaI method on benchmark ImageNet, especially in a highly compressed model.

cs.CV↗

arXiv2022

Spatial-temporal Concept based Explanation of 3D ConvNets

Recent studies have achieved outstanding success in explaining 2D image recognition ConvNets. On the other hand, due to the computation cost and complexity of video data, the explanation of 3D video recognition ConvNets is relatively less studied. In this paper, we present a 3D ACE (Automatic Concept-based Explanation) framework for interpreting 3D ConvNets. In our approach: (1) videos are represented using high-level supervoxels, which is straightforward for human to understand; and (2) the interpreting framework estimates a score for each voxel, which reflects its importance in the decision procedure. Experiments show that our method can discover spatial-temporal concepts of different importance-levels, and thus can explore the influence of the concepts on a target task, such as action classification, in-depth. The codes are publicly available.

cs.CV↗