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Licheng Jiao

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24 papers

2 author rows

AAAI Conference 2026 Conference Paper

Evolving Semantic Propagation for Aerial Semantic 3D Gaussian Splatting

Zihan Gao
Lingling Li
Xu Liu
Fang Liu
Licheng Jiao
Puhua Chen
Wenping Ma
Shuyuan Yang

Semantic understanding of large-scale aerial scenes represents a critical challenge in 3D computer vision, hindered by the prohibitive cost of dense annotation. This paper introduces EvoPropGS, a novel approach for the semantic segmentation of 3D Gaussian Splatting models that requires only minimal supervision. Our core insight is to leverage the inherent structural repetitions within aerial environments to propagate semantic information from a sparse set of annotations across the entire 3D scene. Our approach constructs a prompt library by pairing SAM-generated mask candidates with DINOv2 feature embeddings from annotated views. For unannotated regions, we generate pseudo-labels by matching region proposals with these featured prompts via cosine similarity. We then formulate optimal prompt selection as a discrete optimization problem solved via evolutionary search, guided by our novel fitness function that evaluates both 3D consistency and 2D semantic coherence. Extensive experiments demonstrate that EvoPropGS achieves accurate segmentation with only 2 percent annotated pixels.

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AAAI Conference 2026 Conference Paper

HTTrack: Learning to Perceive Targets via Historical Trajectories in Satellite Video Tracking

Jiahao Wang
Fang Liu
Licheng Jiao
Hao Wang
Shuo Li
Xinyi Wang
Lingling Li
Puhua Chen

In recent years, the rapid progress of deep learning has driven notable advancements in satellite video tracking, a critical task for applications such as environmental monitoring, disaster management, and defense. Despite these strides, existing approaches remain constrained by their inability to handle dynamic challenges, such as target appearance variations, complex motion patterns, and occlusions. Traditional methods often suffer from static template matching or overly complex update mechanisms, compromising their robustness and practicality in real-world scenarios. To address these limitations, we propose a paradigm shift in satellite video tracking by integrating historical trajectory knowledge with visual features. This fusion enhances the tracker's perceptual understanding of targets over time, enabling more adaptive and resilient tracking. By aligning spatial, temporal, and cross-modal information, our approach effectively bridges the gap between fragmented observations and coherent tracking performance, even under challenging conditions like small target detection and cluttered backgrounds. Extensive experiments conducted on multiple satellite video tracking benchmarks demonstrate the superiority of our method, with HTTrack achieving success rates of 51.5% on SV248S, 52.9% on SatSOT, and 32.6% on VISO, significantly outperforming state-of-the-art trackers and marking a step forward in achieving robust, accurate, and scalable satellite video tracking.

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AAAI Conference 2026 Conference Paper

SOAR: Semi-Supervised Open-Vocabulary Aerial Object Detection via Dual-Aware Enhanced Prior Denoising

Xu Liu
Yihong Huang
Dan Zhang
Lingling Li
Long Sun
Licheng Jiao

Open-Vocabulary Object Detection (OVOD) shows promise in remote sensing (RS), but due to its unique value, there are challenges such as the predominance of background regions, sparse labels, limited semantic information, and difficulties in semi-supervised training. To tackle these challenges, we propose the Semi-Supervised Open-Vocabulary Aerial Object Detection with Dual-Perception Prior Denoising (SOAR), which explicitly models the background embeddings of each scene to indirectly construct foreground priors, thereby capitalizing on the abundant background information present in RS imagery. We further introduce a query enhancement module that integrates language and foreground prior information to enhance the effectiveness of query selection and feature augmentation. During the decoding stage of semi-supervised training, we perform denoising and reconstruction of the foreground priors to generate pseudo-labels that support the training process. Additionally, we address the sparsity of label information through expansion and aggregation techniques, further improving model performance. Experimental evaluations reveal that, in the open-vocabulary object detection task on the DIOR dataset, our method achieves a mean Average Precision (mAP) of 68.5% and Harmonic Mean (HM) of 55.9%, outperforming the previous state-of-the-art model’s mAP of 61.6% and HM of 53.6%. Our approach offers a novel solution to the open-vocabulary challenge in aerial object detection.

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IJCAI Conference 2025 Conference Paper

Language-Guided Hybrid Representation Learning for Visual Grounding on Remote Sensing Images

Biao Liu
Xu Liu
Lingling Li
Licheng Jiao
Fang Liu
Xinyu Sun
Youlin Huang

Visual grounding (VG) refers to detecting the specific objects in images based on linguistic expressions, and it has profound significance in the advanced interpretation of natural images. In remote sensing image interpretation, visual grounding is limited by characteristics such as the complex scenes and diverse object sizes. To solve this problem, we propose a novel remote sensing visual grounding (RSVG) framework, named language-guided hybrid representation learning Transformer (LGFormer). Specifically, we designed a multimodal dual-encoder Transformer structure called the adaptive multimodal feature fusion module. This structure innovatively integrates text and visual features as hybrid queries, enabling early-stage decoding queries to perceive the target position accurately. Then, the different modal information from the dual encoders is aggregated by hybrid queries to obtain the final object embedding for coordinate regression. Besides, a multi-scale cross-modal feature enhancement module (MSCM) is designed to enhance the self-representation of the extracted text and visual features and align them semantically. As for the hybrid queries, we use linguistic guidance to select visual features as the visual part and sentence-level features as the textual part. Finally, the LGFormer model we designed achieved the best results compared to existing models on the DIOR-RSVG and OPT-RSVG datasets.

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IJCAI Conference 2025 Conference Paper

RegionMatch: Pixel-Region Collaboration for Semi-Supervised Semantic Segmentation in Remote Sensing Images

Xiaoqian Zhu
Xiangrong Zhang
Tianyang Zhang
Chaowei Fang
Xu Tang
Licheng Jiao

Semi-supervised semantic segmentation (S4) has shown significant promise in reducing the burden of labor-intensive data annotation. However, existing methods mainly rely on pixel-level information, neglecting the strong region consistency inherent in remote sensing images (RSIs), which limits their effectiveness in handling the complex and diverse backgrounds of RSIs. To address this, we propose RegionMatch, a novel approach that leverages unlabeled data from a fresh object-level perspective, which is more tailored to the nature of semantic segmentation. We design the Pixel-Region Synergy Pseudo-Labeling strategy, which explicitly injects object-level contextual information into the S4 pipeline and promotes knowledge collaboration between pixel and region perspectives for generating high-quality pseudo-labels. In addition, we propose the Region Structure-Aware Correlation Consistency, which models object-level relationships by establishing inter-region correlations across images and pixel correlations within regions, providing more effective supervision signals for unlabeled data. Experimental results demonstrate that RegionMatch outperforms state-of-the-art methods on multiple authoritative remote sensing datasets, highlighting its superiority in the RSIs.

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ICLR Conference 2025 Conference Paper

Rethinking Multiple-Instance Learning From Feature Space to Probability Space

Zhaolong Du
Shasha Mao
Xuequan Lu
Mengnan Qi
Yimeng Zhang
Jing Gu
Licheng Jiao

Multiple-instance learning (MIL) was initially proposed to identify key instances within a set (bag) of instances when only one bag-level label is provided. Current deep MIL models mostly solve multi-instance problem in feature space. Nevertheless, with the increasing complexity of data, we found this paradigm faces significant risks in representation learning stage, which could lead to algorithm degradation in deep MIL models. We speculate that the degradation issue stems from the persistent drift of instances in feature space during learning. In this paper, we propose a novel Probability-Space MIL network (PSMIL) as a countermeasure. In PSMIL, a self-training alignment strategy is introduced in probability space to cope with the drift problem in feature space, and the alignment target objective is proven mathematically optimal. Furthermore, we reveal that the widely-used attention-based pooling mechanism in current deep MIL models is easily affected by the perturbation in feature space and further introduce an alternative called probability-space attention pooling. It effectively captures the key instance in each bag from feature space to probability space, and further eliminates the impact of selection drift in the pooling stage. To summarize, PSMIL seeks to solve a MIL problem in probability space rather than feature space. Experimental results illustrate that PSMIL could potentially achieve performance close to supervised learning level in complex tasks (gap within 5\%), with the incremental alignment in propability space bring more than 19\% accuracy improvements for current existing mainstream models in simulated CIFAR datasets. For existing publicly available MIL benchmarks/datasets, attention in probability space also achieves competitive performance to the state-of-the-art deep MIL models. Codes are available at \url{https://github.com/LMBDA-design/PSAMIL}.

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JBHI Journal 2025 Journal Article

Self-Supervised, Non-Contact Heartbeat Detection Based on Ballistocardiograms Utilizing Physiological Information Guidance

Changzhe Jiao
Aoyu Yang
Hantao Zhao
Ruhan Yi
Shuiping Gou
Yu Sha
Wanshun Wen
Licheng Jiao

Ballistocardiograms (BCG) is a passive, non-contact heart rate detection technology that requires no action on the part of the individual. However, during the BCG signal acquisition process, the surface pressure generated by cardiac contraction is easily disturbed by external factors, and as people's health deteriorates, the j-peak (the main peak of the BCG signal) is no longer prominent. Our aim is to establish a non-contact, self-supervised heart rate detection method based on physiological information, to improve the accuracy and robustness of BCG heart rate detection under wider and more adverse conditions. The algorithm is guided by the heart rate estimation based on BCG itself, thereby reconstructing a signal with physiological significance. We also propose a heartbeat mapping algorithm based on Bidirectional Long Short-Term Memory Network (BiLSTM) for extracting global deep features, achieving real-time heartbeat prediction, and eliminating local deviations brought about by reconstruction. To verify the effectiveness of the proposed method, this paper evaluated 40 young subjects and 4 elderly subjects. Compared with the existing state-of-the-art methods, beat-to-beat heart rate estimation and heartbeat detection both performed excellently, surpassing most methods using precise labels. The experimental results show that the proposed method achieves effective heartbeat detection, demonstrating robustness and effectiveness in the face of unavoidable noise and variations.

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AAAI Conference 2024 Conference Paper

ViLT-CLIP: Video and Language Tuning CLIP with Multimodal Prompt Learning and Scenario-Guided Optimization

Hao Wang
Fang Liu
Licheng Jiao
Jiahao Wang
Zehua Hao
Shuo Li
Lingling Li
Puhua Chen

Pre-trained vision-language(V-L) models such as CLIP have demonstrated impressive Zero-Shot performance in many downstream tasks. Since adopting contrastive video-text pairs methods like CLIP to video tasks is limited by its high cost and scale, recent approaches focus on efficiently transferring the image-based CLIP to the video domain. A major finding is that fine-tuning the pre-trained model to achieve strong fully supervised performance leads to low zero shot, few shot, and base to novel generalization. Instead, freezing the backbone network to maintain generalization ability weakens fully supervised performance. Otherwise, no single prompt tuning branch consistently performs optimally. In this work, we proposed a multimodal prompt learning scheme that balances supervised and generalized performance. Our prompting approach contains three sections: 1) Independent prompt on both the vision and text branches to learn the language and visual contexts. 2) Inter-modal prompt mapping to ensure mutual synergy. 3) Reducing the discrepancy between the hand-crafted prompt (a video of a person doing [CLS]) and the learnable prompt, to alleviate the forgetting about essential video scenarios. Extensive validation of fully supervised, zero-shot, few-shot, base-to-novel generalization settings for video recognition indicates that the proposed approach achieves competitive performance with less commute cost.

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ICLR Conference 2023 Conference Paper

Delving into Semantic Scale Imbalance

Yanbiao Ma
Licheng Jiao
Fang Liu 0001
Yu Xin Li
Shuyuan Yang 0001
Xu Liu 0006

Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is equivalent to the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias.

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NeurIPS Conference 2023 Conference Paper

RGMIL: Guide Your Multiple-Instance Learning Model with Regressor

Zhaolong Du
Shasha Mao
Yimeng Zhang
Shuiping Gou
Licheng Jiao
Lin Xiong

In video analysis, an important challenge is insufficient annotated data due to the rare occurrence of the critical patterns, and we need to provide discriminative frame-level representation with limited annotation in some applications. Multiple Instance Learning (MIL) is suitable for this scenario. However, many MIL models paid attention to analyzing the relationships between instance representations and aggregating them, but neglecting the critical information from the MIL problem itself, which causes difficultly achieving ideal instance-level performance compared with the supervised model. To address this issue, we propose the $\textbf{\textit{Regressor-Guided MIL network} (RGMIL)}$, which effectively produces discriminative instance-level representations in a general multi-classification scenario. In the proposed method, we make full use of the $\textit{regressor}$ through our newly introduced $\textit{aggregator}$, $\textbf{\textit{Regressor-Guided Pooling} (RGP)}$. RGP focuses on simulating the correct inference process of humans while facing similar problems without introducing new parameters, and the MIL problem can be accurately described through the critical information from the $\textit{regressor}$ in our method. In experiments, RGP shows dominance on more than 20 MIL benchmark datasets, with the average bag-level classification accuracy close to 1. We also perform a series of comprehensive experiments on the MMNIST dataset. Experimental results illustrate that our $\textit{aggregator}$ outperforms existing methods under different challenging circumstances. Instance-level predictions are even possible under the guidance of RGP information table in a long sequence. RGMIL also presents comparable instance-level performance with S-O-T-A supervised models in complicated applications. Statistical results demonstrate the assumption that a MIL model can compete with a supervised model at the instance level, as long as a structure that accurately describes the MIL problem is provided. The codes are available on $\url{https: //github. com/LMBDA-design/RGMIL}$.

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IJCAI Conference 2022 Conference Paper

Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information

Guanchun Wang
Xiangrong Zhang
Zelin Peng
Xu Tang
Huiyu Zhou
Licheng Jiao

Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e. g. , categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i. e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information. Based on this observation, we here propose a negative deterministic information (NDI) based method for improving WSOD, namely NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and exploiting. In the collecting stage, we design several processes to identify and distill the NDI from negative instances online. In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively. Experimental results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO show that our method achieves satisfactory performance.

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AAAI Conference 2022 Conference Paper

Self-Training Multi-Sequence Learning with Transformer for Weakly Supervised Video Anomaly Detection

Shuo Li
Fang Liu
Licheng Jiao

Weakly supervised Video Anomaly Detection (VAD) using Multi-Instance Learning (MIL) is usually based on the fact that the anomaly score of an abnormal snippet is higher than that of a normal snippet. In the beginning of training, due to the limited accuracy of the model, it is easy to select the wrong abnormal snippet. In order to reduce the probability of selection errors, we first propose a Multi-Sequence Learning (MSL) method and a hinge-based MSL ranking loss that uses a sequence composed of multiple snippets as an optimization unit. We then design a Transformer-based MSL network to learn both video-level anomaly probability and snippet-level anomaly scores. In the inference stage, we propose to use the video-level anomaly probability to suppress the fluctuation of snippet-level anomaly scores. Finally, since VAD needs to predict the snippet-level anomaly scores, by gradually reducing the length of selected sequence, we propose a self-training strategy to gradually refine the anomaly scores. Experimental results show that our method achieves significant improvements on ShanghaiTech, UCF-Crime, and XD-Violence.

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EAAI Journal 2021 Journal Article

Graph Convolutional Neural Networks with Geometric and Discrimination information

Ronghua Shang
Yang Meng
Weitong Zhang
Fanhua Shang
Licheng Jiao
Shuyuan Yang

In recent years, geometric deep learning methods have been proposed, which are called Graph Convolutional Neural Networks (GCNNs). GCNNs not only can extract effective features like the classical CNN, but also can effectively reflect the true geometric structure of original data. Although GCNNs consider the geometric structure of original data, they construct the same feature graph to perform graph convolution, and ignore the difference between the local structures of different samples. Therefore, a novel Graph Convolutional Neural Network with Geometric and Discrimination information (GDGCNN) is proposed, which integrates traditional machine learning ideas to further improve the performance of feature extraction. In order to exploit differences between the local structures of different samples and make full use of the geometric structure of original data, GDGCNN constructs different feature graphs for different training batches to fully exploit the local geometry of data. Moreover, the discriminant regularization is introduced into GDGCNN to effectively utilize the discriminant information contained in original data. Therefore, GDGCNN has good discriminative ability and robustness. The experimental results show that GDGCNN can perform feature extraction tasks very well, and it is superior to some existing methods for classification in terms of accuracy and F1-Score.

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JBHI Journal 2021 Journal Article

Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression

Changzhe Jiao
Chao Chen
Shuiping Gou
Dong Hai
Bo-Yu Su
Marjorie Skubic
Licheng Jiao
Alina Zare

Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The proposed deep regression model provides an effective solution to the existing challenges in BCG heart rate estimation, such as the mismatch between the BCG signals and ground-truth reference, multi-sensor fusion and effective time series feature learning. Allowing label uncertainty in the estimation can reduce the manual cost of data annotation while further improving the heart rate estimation performance. Compared with the state-of-the-art BCG heart rate estimation methods, the strong fitting and generalization ability of the proposed deep regression model maintains better robustness to noise ( e. g. , sensor noise) and perturbations ( e. g. , body movements) in the BCG signals and provides a more reliable solution for long term heart rate monitoring.

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IJCAI Conference 2020 Conference Paper

AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation

Gege Zhang
Qinghua Ma
Licheng Jiao
Fang Liu
Qigong Sun

3D point cloud semantic segmentation has attracted wide attention with its extensive applications in autonomous driving, AR/VR, and robot sensing fields. However, in existing methods, each point in the segmentation results is predicted independently from each other. This property causes the non-contiguity of label sets in three-dimensional space and produces many noisy label points, which hinders the improvement of segmentation accuracy. To address this problem, we first extend adversarial learning to this task and propose a novel framework Attention Adversarial Networks (AttAN). With high-order correlations in label sets learned from the adversarial learning, segmentation network can predict labels closer to the real ones and correct noisy results. Moreover, we design an additive attention block for the segmentation network, which is used to automatically focus on regions critical to the segmentation task by learning the correlation between multi-scale features. Adversarial learning, which explores the underlying relationship between labels in high-dimensional space, opens up a new way in 3D point cloud semantic segmentation. Experimental results on ScanNet and S3DIS datasets show that this framework effectively improves the segmentation quality and outperforms other state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor

Yuanyuan Liu
Fanhua Shang
Licheng Jiao

Recently, research on variance reduced incremental gradient descent methods (e. g. , SAGA) has made exciting progress (e. g. , linear convergence for strongly convex (SC) problems). However, existing accelerated methods (e. g. , point-SAGA) suffer from drawbacks such as inflexibility. In this paper, we design a novel and simple momentum to accelerate the classical SAGA algorithm, and propose a direct accelerated incremental gradient descent algorithm. In particular, our theoretical result shows that our algorithm attains a best known oracle complexity for strongly convex problems and an improved convergence rate for the case of n>=L/\mu. We also give experimental results justifying our theoretical results and showing the effectiveness of our algorithm.

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AAAI Conference 2019 Conference Paper

Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1,+1}

Qigong Sun
Fanhua Shang
Kang Yang
Xiufang Li
Yan Ren
Licheng Jiao

The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance. Thus, DNNs cannot be efficiently applied to mobile phones and embedded devices, which seriously limits their applicability in industry applications. To address this issue, we propose a novel encoding scheme of using {−1, +1} to decompose quantized neural networks (QNNs) into multibranch binary networks, which can be efficiently implemented by bitwise operations (xnor and bitcount) to achieve model compression, computational acceleration and resource saving. Based on our method, users can easily achieve different encoding precisions arbitrarily according to their requirements and hardware resources. The proposed mechanism is very suitable for the use of FPGA and ASIC in terms of data storage and computation, which provides a feasible idea for smart chips. We validate the effectiveness of our method on both large-scale image classification tasks (e. g. , ImageNet) and object detection tasks. In particular, our method with lowbit encoding can still achieve almost the same performance as its full-precision counterparts.

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EAAI Journal 2018 Journal Article

Dual-graph regularized non-negative matrix factorization with sparse and orthogonal constraints

Yang Meng
Ronghua Shang
Licheng Jiao
Wenya Zhang
Shuyuan Yang

Semi-supervised Non-negative Matrix Factorization (NMF) can not only utilize a fraction of label information, but also effectively learn local information of the objectives, such as documents and faces. Semi-supervised NMF is an efficient technique for dimensionality reduction of high dimensional data. In this paper, we propose a novel semi-supervised NMF, called Dual-graph regularized Non-negative Matrix Factorization with Sparse and Orthogonal constraints (SODNMF). Dual-graph model is added into semi-supervised NMF, and the manifold structures of the data space and the feature space are taken into account simultaneously. In addition, the sparse constraint is used in SODNMF, which can simplify the calculation and accelerate the processing speed. The most important is that SODNMF makes use of bi-orthogonal constraints, which can avoid the non-correspondence between images and basic vectors. Therefore, it can effectively enhance the discrimination and the exclusivity of clustering, and improve the clustering performance. We give the objective function, the iterative updating rules and the convergence proof. Empirical experiments demonstrate encouraging results of our novel algorithm in comparison to four algorithms within some state-of-the-art algorithms through a set of evaluations based on three real datasets.

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NeurIPS Conference 2017 Conference Paper

Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds

Yuanyuan Liu
Fanhua Shang
James Cheng
Hong Cheng
Licheng Jiao

In this paper, we propose an accelerated first-order method for geodesically convex optimization, which is the generalization of the standard Nesterov's accelerated method from Euclidean space to nonlinear Riemannian space. We first derive two equations and obtain two nonlinear operators for geodesically convex optimization instead of the linear extrapolation step in Euclidean space. In particular, we analyze the global convergence properties of our accelerated method for geodesically strongly-convex problems, which show that our method improves the convergence rate from O((1-\mu/L)^{k}) to O((1-\sqrt{\mu/L})^{k}). Moreover, our method also improves the global convergence rate on geodesically general convex problems from O(1/k) to O(1/k^{2}). Finally, we give a specific iterative scheme for matrix Karcher mean problems, and validate our theoretical results with experiments.

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EAAI Journal 2014 Journal Article

Change detection in SAR images by artificial immune multi-objective clustering

Ronghua Shang
Liping Qi
Licheng Jiao
Rustam Stolkin
Yangyang Li

This paper addresses the problem of unsupervised change detection in Synthetic Aperture Radar (SAR) images. Previous approaches have used evolutionary clustering optimization methods, which can suffer from reduced accuracy, because they often use only a single objective function and can easily become trapped at locally optimal values. To overcome these difficulties, we propose a new approach which combines the artificial immune system (AIS) theory with a multi-objective optimization algorithm. First, the self-adaptive artificial immune multi-objective algorithm is adopted to pre-sort the difference image. During this procedure, the difference image is categorized into three classes – changed class, unchanged class and uncertain samples. Second, based on wavelet decomposition to extract features from the difference image, the immune clonal multi-objective clustering algorithm is used to search for the optimal clustering centers of uncertain samples, labeling them as changed or unchanged. Experimental comparisons with four state-of-the-art approaches show that the proposed algorithm can obtain a higher accuracy, is more robust to noise, and finds solutions which are more globally optimal. Additionally, the proposed algorithm can improve the local search ability for the optimal solutions and produces better cluster centers.

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EAAI Journal 2014 Journal Article

Improved Bandelet with heuristic evolutionary optimization for image compression

Shuyuan Yang
Shigang Wang
Zhengkang Liu
Min Wang
Licheng Jiao

As an efficient geometric analysis tool, Bandelet has exhibited enormous potential in image compression for its capability in capturing the geometrical structure in images. However, in available implementation of Bandelet, the optimal geometric flows are determined by a coarse and exhaustive search, which will degrade the performance of Bandelet in an accurate representation of images. In this paper, we advance a new image compression approach based on Laplacian Pyramid (LP) and improved Bandelet, where a Heuristic Memetic Algorithm (HMA) is proposed to locate accurate geometric flows. The chromosome is defined to represent geometric flows, and local and heuristic evolution operators are employed to make fast search possible. The improved Bandelet is used to compress the high-frequency band of the image decomposed by LP, and the low-frequency band is coded by Set Partitioning In Hierarchical Trees (SPIHT) algorithm. Some experiments are taken on some natural images and remote sensing images, and the results demonstrate that our proposed scheme outperforms SPIHT and the second generation Bandelet(2 G-Bandelet) in both PSNR and time consumption at low bit rate compression.

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EAAI Journal 2013 Journal Article

Superpixel-wise semi-supervised structural sparse coding classifier for image segmentation

Shuyuan Yang
Yuan Lv
Yu Ren
Licheng Jiao

Sparse coding based classifier (SCC) proves to lead to the state-of-the-art result in pattern recognition. Compared with traditional generative models and discriminative models, it neither casts some assumption on the distribution of data, nor learns a hyperplane to separate samples. However, SCC is characteristic of slow prediction because an l 0 -norm minimization need to be solved to assign the label for each sample. In this paper, we propose a Superpixel-wise Structural Sparse Coding based Classifier (S3CC) for image segmentation. An unsupervised superpixel segmentation is first used to derive the initial labeled samples, and SCC is extended to the semi-supervised pattern where unlabeled samples are incrementally labeled and taken as the dictionary to improve the classification accuracy. Moreover, a neighborhood spatial constraint is cast on the prediction of pixel labels, to avoid the speckle-like mis-segmentation of images. Some experiments are taken on some artificial texture images, to investigate the segmentation result of our proposed S3CC. Some aspects including (1) Comparison of S3CC with SCC, (2) Comparisons of S3CC with and without spatial constraint, (3) Comparison of S3CC with semi-supervised S3CC, are tested, and the results prove the efficiency and superiority of S3CC to its counterparts.

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EAAI Journal 2012 Journal Article

Evolution-enhanced multiscale overcomplete dictionaries learning for image denoising

Shuyuan Yang
Min Wang
Meirong Wei
Licheng Jiao

In this paper, a multiscale overcomplete dictionary learning approach is proposed for image denoising by exploiting the multiscale property and sparse representation of images. The images are firstly sparsely represented by a translation invariant dictionary and then the coefficients are denoised using some learned multiscale dictionaries. Dictionaries learning can be reduced to a non-convex l 0-norm minimization problem with multiple variables, so an evolution-enhanced algorithm is proposed to alternately optimize the variables. Some experiments are taken on comparing the performance of our proposed method with its counterparts on some benchmark natural images, and the superiorities of our proposed method to its counterparts can be observed in both the visual result and some numerical guidelines.

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NeurIPS Conference 2005 Conference Paper

Response Analysis of Neuronal Population with Synaptic Depression

Wentao Huang
Licheng Jiao
Shan Tan
Maoguo Gong

In this paper, we aim at analyzing the characteristic of neuronal population responses to instantaneous or time-dependent inputs and the role of synapses in neural information processing. We have derived an evolution equation of the membrane potential density function with synaptic depression, and obtain the formulas for analytic computing the response of instantaneous re rate. Through a technical analysis, we arrive at several signi cant conclusions: The background inputs play an important role in information processing and act as a switch betwee temporal integration and coincidence detection. the role of synapses can be regarded as a spatio-temporal lter; it is important in neural information processing for the spatial distribution of synapses and the spatial and temporal relation of inputs. The instantaneous input frequency can affect the response amplitude and phase delay.

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