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Jinye Peng

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13 papers

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EAAI Journal 2025 Journal Article

Block information strategy for multi-modal remote sensing image registration

Yameng Hong
Chengcai Leng
Beihua Liu
Jinye Peng
Irene Cheng
Anup Basu

Registration of multi-modal remote sensing image pairs (MRSI) is challenging given the distinct imaging mechanisms of multi-modal data sources, which lead to substantial geometric and radiometric distortions and inaccuracies in correspondences. To tackle this issue, we propose a novel approach that integrates local image information into feature representations through the design of local regions and the extraction of local information. The latter comprises of two key components: rank-based feature redistribution and residual information extraction utilizing a pyramid-like structure of local patches. This enhanced feature representation technique, termed Reinforced Local Information of LSS (RLILSS), embeds local information to improve the performance of the Local Self-Similarity (LSS)-based framework for MRSI registration. RLILSS strengthens feature characterization across various regions and addresses the limitations of supplementary information. This enables more reliable correspondences between images. Experimental results show that the proposed method achieves higher accuracy and better registration across diverse multi-modal datasets. Detailed analyses confirm its superiority over state-of-the-art methods in both accuracy and robustness. This approach holds significant potential for applications in automatic geographic registration and disaster area reconstruction.

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EAAI Journal 2025 Journal Article

Dual graph-regularized low-rank representation for hyperspectral image denoising

Chengcai Leng
Mingpei Tang
Zhao Pei
Jinye Peng
Anup Basu

Hyperspectral images have a wide range of applications in many fields. However, when hyperspectral images are captured by spectrometers, there is inevitably considerable noise, which affects subsequent research. In recent years, many hyperspectral image denoising methods based on low-rank representations have been proposed. Artificial intelligence denoising methods are also popular. However, the research on multi noise denoising is rarely mentioned, and most literatures only focus on one noise in hyperspectral images. Thus, we propose a denoising model for hyperspectral image based on dual graph-regularized low-rank representation, which can not only reduce multiple types of noise simultaneously, but also preserves details of the original image. In particular, this is the first time that the dual low-rank representation and dual graph regularizations are used on hyperspectral images. We solve this method using the linearized alternating direction method with adaptive penalty. Finally, we conduct experiments on simulated and real data sets to verify the effectiveness of our method. The experimental results show that our method can not only effectively remove a variety of mixed noises, but also well retain the details of the image.

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AAAI Conference 2025 Conference Paper

Iterative Self-Training with Class-Aware Text-to-Image Synthesis for Visual Task Learning

Xiang Zhang
Wanqing Zhao
Pengyang Li
Ying Liu
Hangzai Luo
Sheng Zhong
Jinye Peng
Jianping Fan

Generative models are widely used to produce synthetic images with annotations, alleviating the burden of image collection and annotation for training deep visual models. However, challenges such as limited image diversity, noisy pseudo labels, and domain gaps between synthetic and real images often undermine their effectiveness in downstream visual tasks. This paper introduces the Iterative Self-Training with Class-Aware Text-to-Image Synthesis (IST-CATS) framework, which addresses these challenges by integrating a class-aware text-to-image synthesis (CATS) component with an iterative self-training (IST) strategy. CATS innovatively introduces a class-aware chain approach to generate detailed descriptions. These descriptions act as prompts for a diffusion model, enabling the creation of a diverse of images accompanied by distinguishable objects against the background. The generated images can be easily pseudo-labeled by an unsupervised instance segmentation method, and then noisy pseudo labels can be effectively purified by a novel feature similarity-based filtering mechanism. The generated images underpin our IST, which progressively enhances vision models and refines pseudo labels through self-training and our proposed label filtering strategy (LabFilt). LabFilt meticulously improves the quality of pseudo labels by employing class-adaptive techniques at both the pixel and object levels, ensuring refined pseudo-label accuracy. IST-CATS demonstrates superior performance in object detection and semantic segmentation compared to traditional synthetic and semi/weakly-supervised methods, effectively addressing data collection and annotation challenges.

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EAAI Journal 2025 Journal Article

Orthogonal Diversity Nonnegative Matrix Factorization for multi-view clustering

Xinling Zhang
Chengcai Leng
Jinye Peng
Irene Cheng
Anup Basu

In the context of rapid development of artificial intelligence, how to extract valuable information from complex multidimensional data has become a core research problem. Multi-view clustering methods based on non-negative matrix factorization (NMF) are widely used in multi-view data analysis, but still face many challenges in practical applications. Current multi-view clustering methods usually solve the problem of diversity among viewpoints by orthogonalization of view representations. However, they fail to fully utilize the rich features of each viewpoint because data from different viewpoints may be interrelated. In addition, existing methods fail to fully consider the orthogonality between base matrices while emphasizing the diversity of view representations. For this reason, this paper proposes a new orthogonal diversity non-negative matrix factorization method (ODNMF). First, ODNMF explores the orthogonality of the representations of sample pairs between different viewpoints. This approach preserves the characteristics of each perspective and enhances the diversity of data representations. Second, ODNMF orthogonalizes the basis matrix of each viewpoint to reduce redundant features and enhance data interpretability and representation. Finally, ODNMF introduces graph regularization for each view to reveal the intrinsic geometric and structural information of features. Experimental results show that ODNMF significantly outperforms existing state-of-the-art algorithms on seven datasets.

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EAAI Journal 2024 Journal Article

Bayesian non-negative matrix factorization with Student’s t-distribution for outlier removal and data clustering

Ruixue Yuan
Chengcai Leng
Shuang Zhang
Jinye Peng
Anup Basu

Non-negative Matrix Factorization (NMF) is an effective way to solve the redundancy of non-negative high-dimensional data. Most of the traditional probability-based NMF methods use Gaussian distribution to model the differences between the matrices before and after decomposition. However, the Gaussian distribution is strongly affected by outliers, and it may not fit all datasets accurately when there are no outliers in the data. In this article, we propose a novel Bayesian NMF with the Student’s t-distribution, i. e. , TNMF. specifically, in order to reduce the impact of outliers on the algorithm, we use the Student’s t-distribution to fit the data points instead of the Gaussian distribution. In addition, it is possible to adjust the Degree of Freedom (DF) to make the Student’s t-distribution more flexible than the Gaussian distribution to fit data points when there are no outliers. Next, we combine the Automatic Relevance Determination (ARD) prior in our algorithm to simplify the model and allow for better performance of the algorithm. Finally, the article used 10 datasets to design two kinds of experiments, outlier removal and data clustering. The outlier removal results of this proposed algorithm are significantly better than the other methods, and it performs better in clustering compared to the other methods in the majority of cases.

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EAAI Journal 2024 Journal Article

Feature matching based on Gaussian kernel convolution and minimum relative motion

Kun Wang
Chengcai Leng
Huaiping Yan
Jinye Peng
Zhao Pei
Anup Basu

Feature matching is a necessary and important step for remote sensing image registration, intended to establish reliable point correspondences between two sets of features. In this paper, we propose a feature registration model based on local relative motion, which combines Gaussian kernel convolution with relative motion (GRM) vector to obtain better results by removing wrong matches and improving the inlier point accuracy. We first establish putative matching based on the similarity between local descriptors. Then, the preliminary hypothetical matching point set is filtered using consistency with nearest neighbors among the inlier points to obtain a more accurate motion vector, and to fit the real motion vector through the Gaussian convolution kernel. Finally, we find the displacement between the fitted motion vector and the matching generated motion vector. And combine the displacement with the optimization model to find the inlier point set. Experimental results show that our GRM method outperforms related work, achieving better matching results.

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EAAI Journal 2024 Journal Article

Incremental semi-supervised graph learning NMF with block-diagonal

Xue Lv
Chengcai Leng
Jinye Peng
Zhao Pei
Irene Cheng
Anup Basu

Non-negative matrix factorization (NMF), as a good data dimensionality reduction method, is widely used in the field of image recognition. Incremental non-negative matrix factorization (INMF) as an improvement solves the problem of inefficiency caused by repeated running of data samples during online learning. However, in the traditional incremental non-negative matrix factorization algorithm, the newly added training samples do not contain label information. Some samples may be marked in both the initial sample and the new training sample in the real application scenario. In order to make full use of the label information carried by the dataset, in this paper, we propose a semi-supervised non-negative matrix factorization model for batch incremental data, incremental semi-supervised graph learning NMF with block diagonal (ISGDNMF). The model is divided into three cases according to the degree of label-carrying for the new batch data: all label-carrying, no label-carrying, and partial label-carrying. The label information is also used to add a diagonal structure to the coefficient matrix, which makes it possible to have stronger discriminatory ability and to distinguish different classes of images more easily. And graph regularization information is added in order to maintain the spatial-geometric structure of the data. Experiments on six image datasets show that this algorithm has superior performance relative to the other seven NMF-based algorithms.

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AAAI Conference 2022 Conference Paper

Class Guided Channel Weighting Network for Fine-Grained Semantic Segmentation

Xiang Zhang
Wanqing Zhao
Hangzai Luo
Jinye Peng
Jianping Fan

Deep learning has achieved promising performance on semantic segmentation, but few works focus on semantic segmentation at the fine-grained level. Fine-grained semantic segmentation requires recognizing and distinguishing hundreds of sub-categories. Due to the high similarity of different sub-categories and large variations in poses, scales, rotations, and color of the same sub-category in the fine-grained image set, the performance of traditional semantic segmentation methods will decline sharply. To alleviate these dilemmas, a new approach, named Class Guided Channel Weighting Network (CGCWNet), is developed in this paper to enable finegrained semantic segmentation. For the large intra-class variations, we propose a Class Guided Weighting (CGW) module, which learns the image-level fine-grained category probabilities by exploiting second-order feature statistics, and use them as global information to guide semantic segmentation. For the high similarity between different sub-categories, we specially build a Channel Relationship Attention (CRA) module to amplify the distinction of features. Furthermore, a Detail Enhanced Guided Filter (DEGF) module is proposed to refine the boundaries of object masks by using an edge contour cue extracted from the enhanced original image. Experimental results on PASCAL VOC 2012 and six fine-grained image sets show that our proposed CGCWNet has achieved state-of-the-art results.

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IJCAI Conference 2021 Conference Paper

Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction

Ruijing Yang
Ziyu Guan
Zitong Yu
Xiaoyi Feng
Jinye Peng
Guoying Zhao

Automatic pain recognition is paramount for medical diagnosis and treatment. The existing works fall into three categories: assessing facial appearance changes, exploiting physiological cues, or fusing them in a multi-modal manner. However, (1) appearance changes are easily affected by subjective factors which impedes objective pain recognition. Besides, the appearance-based approaches ignore long-range spatial-temporal dependencies that are important for modeling expressions over time; (2) the physiological cues are obtained by attaching sensors on human body, which is inconvenient and uncomfortable. In this paper, we present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. The framework is able to capture both local and long-range dependencies via the proposed attention mechanism for the learned appearance representations, which are further enriched by temporally attended physiological cues (remote photoplethysmography, rPPG) that are recovered from videos in the auxiliary task. This framework is dubbed rPPG-enriched Spatio-Temporal Attention Network (rSTAN) and allows us to establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases. It demonstrates that rPPG predictions can be used as an auxiliary task to facilitate non-contact automatic pain recognition.

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IJCAI Conference 2018 Conference Paper

Tag-based Weakly-supervised Hashing for Image Retrieval

Ziyu Guan
Fei Xie
Wanqing Zhao
Xiaopeng Wang
Long Chen
Wei Zhao
Jinye Peng

We are concerned with using user-tagged images to learn proper hashing functions for image retrieval. The benefits are two-fold: (1) we could obtain abundant training data for deep hashing models; (2) tagging data possesses richer semantic information which could help better characterize similarity relationships between images. However, tagging data suffers from noises, vagueness and incompleteness. Different from previous unsupervised or supervised hashing learning, we propose a novel weakly-supervised deep hashing framework which consists of two stages: weakly-supervised pre-training and supervised fine-tuning. The second stage is as usual. In the first stage, rather than performing supervision on tags, the framework introduces a semantic embedding vector (sem-vector) for each image and performs learning of hashing and sem-vectors jointly. By carefully designing the optimization problem, it can well leverage tagging information and image content for hashing learning. The framework is general and does not depend on specific deep hashing methods. Empirical results on real world datasets show that when it is integrated with state-of-art deep hashing methods, the performance increases by 8-10%.

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IJCAI Conference 2017 Conference Paper

Deep Multiple Instance Hashing for Object-based Image Retrieval

Wanqing Zhao
Ziyu Guan
Hangzai Luo
Jinye Peng
Jianping Fan

Multi-keyword query is widely supported in text search engines. However, an analogue in image retrieval systems, multi-object query, is rarely studied. Meanwhile, traditional object-based image retrieval methods often involve multiple steps separately and need expensive location labeling for detecting objects. In this work, we propose a weakly-supervised Deep Multiple Instance Hashing (DMIH) framework for object-based image retrieval. DMIH integrates object detection and hashing learning on the basis of a popular CNN model to build the end-to-end relation between a raw image and the binary hashing codes of multiple objects in it. Specifically, we cast the object detection of each object class as a binary multiple instance learning problem where instances are object proposals extracted from multi-scale convolutional feature maps. For hashing training, we sample image pairs to learn their semantic relationships in terms of hash codes of the most probable proposals for owned labels as guided by object predictors. The two objectives benefit each other in learning. DMIH outperforms state-of-the-arts on public benchmarks for object-based image retrieval and achieves promising results for multi-object queries.

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JBHI Journal 2015 Journal Article

Hierarchical classification of large-scale patient records for automatic treatment stratification

Kuizhi Mei
Jinye Peng
Ling Gao
Naiquan Zheng
Jianping Fan

A hierarchical learning algorithm is developed for classifying large-scale patient records, e. g. , categorizing large-scale patient records into large numbers of known patient categories (i. e. , thousands of known patient categories) for automatic treatment stratification. Our hierarchical learning algorithm can leverage tree structure to train more discriminative max-margin classifiers for high-level nodes and control interlevel error propagation effectively. By ruling out unlikely groups of patient categories (i. e. , irrelevant high-level nodes) at an early stage, our hierarchical approach can achieve log-linear computational complexity, which is very attractive for big data applications. Our experiments on one specific medical domain have demonstrated that our hierarchical approach can achieve very competitive results on both classification accuracy and computational efficiency as compared with other state-of-the-art techniques.

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JBHI Journal 2013 Journal Article

Leveraging Social Supports for Improving Personal Expertise on ACL Reconstruction and Rehabilitation

Jinye Peng
Naiquan Zheng
Jianping Fan

In this paper, a social health support system is developed to assist both anterior cruciate ligament (ACL) patients and clinicians on making better decisions and choices for ACL reconstruction and rehabilitation. By providing a good platform to enable more effective sharing of personal expertise and ACL treatments, our social health support system can allow: 1) ACL patients to identify the best matching social groups and locate the most suitable expertise for personal health management; and 2) clinicians to easily locate the best matching ACL patients and learn from well-done treatments, so that they can make better decisions for new ACL patients (who have similar ACL injuries and close social principles with those best matching ACL patients) and prescribe safer and more effective knee rehabilitation treatments.

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