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arXiv 2019年

作者： Fan, Zhun Wang, Zhaojun Li, Wenji Yuan, Yutong You, Yugen Yang, Zhi Sun, Fuzan Ruan, Jie Li, Zhaocheng Department of Electronic Engineering Shantou University Guangdong China Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong China

In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations. However, most state-of-the-art MOEAs show poor performance in balancing them, and can easily cause the working populations to concentrate on a part of regions of the Pareto fronts, leading to a serious imbalanced searching between preserving diversity and achieving convergence. This paper proposes a method which combines a multi-objective to multi-objective (M2M) approach with the push and pull search (PPS) framework, namely PPS-M2M. To be more specific, the proposed algorithm decomposes a CMOP into a set of simple CMOPs. Each simple CMOP corresponds to a sub-population and is solved in a collaborative manner. When dealing with constraints, each sub-population follows a procedure of "ignore the constraints in the push stage and consider the constraints in the pull stage", which helps each working sub-population get across infeasible regions. In order to evaluate the performance of the proposed PPS-M2M, it is compared with the other six algorithms, including M2M, MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP, C-MOEA/D and NSGA-II-CDP on a set of benchmark CMOPs. The experimental results show that the PPS-M2M is significantly better than the other six algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Constraint handling

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Crowd Counting Via Multi-Level Regression With Latent Gaussian Maps

Crowd Counting Via Multi-Level Regression With Latent Gaussi...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Yukang Gao Hua Yang Institution of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai key lab of digital media processing and transmission Shanghai China

Crowd counting still confronts two primary challenges: limited ability to deal with cross density levels caused by fixed density maps and lack of fine-grained or coarse-grained guidance for density estimation. In this paper, a novel end-to-end crowd counting framework via multi-level regression with latent Gaussian maps is proposed, which is consisted of GaussianNet, EstimateNet and Discriminator. GaussianNet is composed of masked Gaussian convolutional blocks and vanillia convolutional layers, to generate latent Gaussian maps adaptively for various density levels. The latent Gaussian maps are then treated as the ground truth density maps for EstimateNet, which outputs density estimations and follows the principle of adversarial learning with Discriminator. Moreover, multi-level losses are combined for density map regression guidance. Extensive experiments on the major public datasets outperform state-of-the-art ones, illustrating the superior validity of the proposed framework.

关键词： Training Convolution Conferences Estimation Acoustics Bayes methods Task analysis

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Attentive Semantic Exploring for Manipulated Face Detection

Attentive Semantic Exploring for Manipulated Face Detection

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Zehao Chen Hua Yang Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China

Face manipulation methods develop rapidly in recent years, whose potential risk to society accounts for the emerging of researches on detection methods. However, due to the diversity of manipulation methods and the high quality of fake images, detection methods suffer from a lack of generalization ability. To solve the problem, we find that segmenting images into semantic fragments could be effective, as discriminative defects and distortions are closely related to such fragments. Besides, to highlight discriminative regions in fragments and to measure contribution to the final prediction of each fragment is efficient for the improvement of generalization ability. Therefore, we propose a novel manipulated face detection method based on Multilevel Facial Semantic Segmentation and Cascade Attention Mechanism. To evaluate our method, we reconstruct two datasets: GGFI and FFMI, and also collect two open-source datasets. Experiments on four datasets verify the advantages of our approach against other state-of-the-arts, especially its generalization ability.

关键词： image segmentation Semantics Diversity methods Feature extraction Face detection Distortion measurement Speech processing

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Spatial-Temporal Constrained Pseudo-labeling for Unsupervised Person Re-identification via GCN Inference 18th

Spatial-Temporal Constrained Pseudo-labeling for Unsupervis...

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18th International Forum of digital Multimedia Communication, IFTC 2021

作者： Ling, Sen Yang, Hua Liu, Chuang Chen, Lin Zhao, Hongtian The Institute of Image Communication and Network Engineering Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789811922657

Most existing unsupervised person re-identification (Re-ID) methods primarily depend on the cluster distance, and merely exploit the available source labeled data to assign pseudo labels for the unannotated data. Whereas, the cluster distance usually fails to adapt to different datasets due to the domain gap. Besides, learning exclusively from the source data can not generate accurate pseudo labels for the lack of the target data information. To address this problem, we propose to exploit the spatial-temporal constraints to facilitate the pseudo label generation process. Specifically, graphs for the labeled source data are constructed and the graph convolution network (GCN) is used to learn graph embeddings. Based on these graph embeddings, the likelihood of linkages between graph nodes is estimated and utilized to assign pseudo labels for the unlabeled data. Then, with the pseudo labels, a smoothed spatial-temporal probability distribution model is generated to amend the likelihood of linkages between graph nodes as well as correct the visual similarity scores for person Re-ID. Finally, we optimize the pseudo label assignment, feature extraction networks, and spatial-temporal model alternatively and iteratively to improve the person Re-ID performance. Comprehensive experiments demonstrate that the proposed method outperforms state-of-the-art methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Graph theory

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Assessment of depth of anesthesia using principal component analysis

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Journal of Biomedical Science and Engineering 2009年第1期2卷 9-15页

作者： Mina Taheri Behzad Ahmadi Rassoul Amirfattahi Mojtaba Mansouri Digital Signal Processing Research Lab Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156-83111 Iran Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran. Correspondence should be addressed to Mina Taheri

A new approach to estimating level of uncon-sciousness based on Principal Component Analysis (PCA) is proposed. The Electroen-cephalogram (EEG) data was captured in both Intensive Care Unit (ICU) and operating room, using different anesthetic drugs. Assuming the central nervous system as a 20-tuple source, window length of 20 seconds is applied to EEG. The mentioned window is considered as 20 nonoverlapping mixed-signals (epoch). PCA algorithm is applied to these epochs, and larg-est remaining eigenvalue (LRE) and smallest remaining eigenvalue (SRE) were extracted. Correlation between extracted parameters (LRE and SRE) and depth of anesthesia (DOA) was measured using Prediction probability (PK). The results show the superiority of SRE than LRE in predicting DOA in the case of ICU and isoflurane, and the slight superiority of LRE than SRE in propofol induction. Finally, a mixture model containing both LRE and SRE could predict DOA as well as Relative Beta Ratio (RBR), which expresses the high capability of the proposed PCA based method in estimating DOA.

关键词： Bispectral Index Depth of Anesthesia Eignevalue Decomposition Principal Component Analysis

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Knowledge Augmented Attention Gating Embedding for Link Prediction

Knowledge Augmented Attention Gating Embedding for Link Pred...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Zewei Chen Shuhong Chen Chen Li Xianwei Zheng Minfan He Xutao Li Department of Electronic Engineering Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou China School of Mathematics Foshan University Foshan China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Knowledge graphs (KGs) use graphical representations to illustrate relations between entities, forming a structured knowledge base. However, KGs often suffer from incompleteness, lacking some entities and their interactions. Current link prediction technologies based on KG embeddings primarily focus on learning entity features and relation patterns from existing KGs to address the incompleteness issue. Yet, these methods encounter the inherent sparsity issue in KGs—uneven distribution of entity connections and scarcity of direct links. This complexity challenges models in identifying intricate, potential associations, thereby compromising link prediction accuracy and overall model performance. In this paper, we introduce a innovative Knowledge Augmented Attention-based Gating Embedding (KAAGE) framework for link prediction. Our model augments knowledge by reversing relations and triples, then integrates both original and augmented structural data through gating units. Extensive experiments on three established benchmark datasets demonstrate the superiority of KAAGE over current models. Our implementation code is available at https://***/22zwChen/KAAGE

关键词： Codes Fuses Semantics Knowledge based systems Knowledge graphs Information processing Predictive models Benchmark testing Data models Complexity theory

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Tomato Plant Diseases Classification Using Statistical Texture Feature and Color Feature

Tomato Plant Diseases Classification Using Statistical Textu...

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International Conference on Computer and Information Science (ACIS)

作者： Chit Su Hlaing Sai Maung Maung Zaw Digital Image Processing Lab University of Computer Studies Mandalay (UCSM) Mandalay Myanmar Faculty of Computer System and Technologies University of Computer Studies Mandalay (UCSM) Mandalay Myanmar

Plant disease classification has been associated with the production of essential food crops and human society. In this paper, we classify tomato plant disease using two different features: texture and color. For a texture feature, we extract statistical texture information (shape, scale and location) of an image from Scale invariant Feature Transform (SIFT) feature. As a main contribution, a new approach is introduced to model the Scale Invariant Feature Transform (SIFT) texture feature by Johnson SB distribution for statistical texture information of an image. The moment method is used to estimate the parameters of Johnson SB distribution. The mathematical representation of SIFT feature is matrix representation and too complex to be applied in image classification. Therefore, we propose a new statistical feature to represent the image in few numbers of dimensions. For a color feature, we extract statistical color information of an image from RGB color channel. The color statistics feature is the combination of mean, standard deviation and moments from degree three to five for each RGB color channel. Our proposed feature is a combination of statistical texture and color features to classify tomato plant disease. The experimental performance on PlantVillage database is compared with state-of-art feature vectors to highlight the advantages of the proposed feature.

关键词： Diseases Feature extraction Histograms image color analysis Agriculture Shape Computational modeling

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Evolutionary neural architecture search for retinal vessel segmentation

arXiv

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arXiv 2020年

作者： Fan, Zhun Wei, Jiahong Zhu, Guijie Mo, Jiajie Li, Wenji Department of Electronic and Information Engineering Shantou University Shantou515063 China Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou515063 China

The accurate retinal vessel segmentation (RVS) is of great significance to assist doctors in the diagnosis of ophthalmology diseases and other systemic diseases. Manually designing a valid neural network architecture for retinal vessel segmentation requires high expertise and a large workload. In order to improve the performance of vessel segmentation and reduce the workload of manually designing neural network, we propose novel approach which applies neural architecture search (NAS) to optimize an encoder-decoder architecture for retinal vessel segmentation. A modified evolutionary algorithm is used to evolve the architectures of encoder-decoder framework with limited computing resources. The evolved model obtained by the proposed approach achieves top performance among all compared methods on the three datasets, namely DRIVE, STARE and CHASE_DB1, but with much fewer parameters. Moreover, the results of cross-training show that the evolved model is with considerable scalability, which indicates a great potential for clinical disease diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Ophthalmology

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Adaptive Time-Varying Graph Learning for Traffic Flow Data Based on Anomaly Moment Detection

Adaptive Time-Varying Graph Learning for Traffic Flow Data B...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Shuhong Chen Zewei Chen Chen Li Xianwei Zheng Minfan He Xutao Li Department of Electronic Engineering Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou China School of Mathematics and Big Data Foshan University Foshan China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

This paper proposes an adaptive time-varying graph structure construction strategy for traffic flow data based on anomaly moment detection, which is capable of identifying anomaly moments within traffic flow signals and adaptively dividing the temporal dimension to construct corresponding graph representations for distinct traffic patterns. Specifically, we leverage a threshold determination method that incorporates the smoothness and energy of temporal difference traffic flow signals to identify anomaly moments in the traffic flow. Upon detecting these anomaly moments, we utilize them as segmentation boundaries along the timeline, adaptively partitioning the continuous time series into multiple dynamic and relatively stable subsequences. Within each subsequence, we apply a time-varying graph learning method to independently construct a graph structure that reflects the traffic flow characteristics of that specific period, thereby achieving an adaptive time-varying property in the traffic flow graph structure. Experimental results demonstrate that the proposed method exhibits reliable performance in detecting anomaly moments in traffic flow, and the adaptive time-varying graph structures accurately reflect the varying characteristics of traffic flow across different time periods.

关键词： Learning systems Time series analysis Asia Information processing Traffic control Data models Reliability Flow graphs Anomaly detection Periodic structures

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User interaction in region-based color image segmentation 3rd

User interaction in region-based color image segmentation

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3rd International Conference on Visual Information Systems, VISUAL 1999

作者： Ikonomakis, Nicolaos Plataniotis, Kostas N. Venetsanopoulos, Anastasios N. Department of Electrical and Computer Engineering Digital Signal and Image Processing Lab University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada School of Computer Science Ryerson Polytechnic University 350 Victoria Street TorontoONM5B 2K3 Canada

ISBN: (纸本)3540660798

An interactive color image segmentation technique is presented for use in applications where the segmented regions correspond to meaningful objects, such as for image retrieval. The proposed technique utilizes the perceptual HSI (hue,saturation,intensity) color space. The scheme incorporates user interaction so that the best possible results can be achieved. Interaction with the user allows the segmentation algorithm to start efficiently and to refine the results. Interaction is performed for the key images (usually the first image or those where new ojects enter the scene) of the video-telephony sequence. The user is allowed to identify on the screen the relevant regions by marking their seeds. The user guidance can be given by the sender, by the receiver, or by both. The effectiveness of the algorithm is found to be much improved over techniques used in the past. © Springer-Verlag Berlin Heidelberg 1999.

关键词： image retrieval

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