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https://www.readbyqxmd.com/read/28092596/a-general-framework-of-dynamic-constrained-multiobjective-evolutionary-algorithms-for-constrained-optimization
#1
Sanyou Zeng, Ruwang Jiao, Changhe Li, Xi Li, Jawdat S Alkasassbeh
A novel multiobjective technique is proposed for solving constrained optimization problems (COPs) in this paper. The method highlights three different perspectives: 1) a COP is converted into an equivalent dynamic constrained multiobjective optimization problem (DCMOP) with three objectives: a) the original objective; b) a constraint-violation objective; and c) a niche-count objective; 2) a method of gradually reducing the constraint boundary aims to handle the constraint difficulty; and 3) a method of gradually reducing the niche size aims to handle the multimodal difficulty...
January 16, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28092591/relational-regularized-discriminative-sparse-learning-for-alzheimer-s-disease-diagnosis
#2
Baiying Lei, Peng Yang, Tianfu Wang, Siping Chen, Dong Ni
Accurate identification and understanding informative feature is important for early Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel discriminative sparse learning method with relational regularization to jointly predict the clinical score and classify AD disease stages using multimodal features. Specifically, we apply a discriminative learning technique to expand the class-specific difference and include geometric information for effective feature selection. In addition, two kind of relational information are incorporated to explore the intrinsic relationships among features and training subjects in terms of similarity learning...
January 16, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28092588/a-many-objective-evolutionary-algorithm-using-a-one-by-one-selection-strategy
#3
Yiping Liu, Dunwei Gong, Jing Sun, Yaochu Jin
Most existing multiobjective evolutionary algorithms experience difficulties in solving many-objective optimization problems due to their incapability to balance convergence and diversity in the high-dimensional objective space. In this paper, we propose a novel many-objective evolutionary algorithm using a one-by-one selection strategy. The main idea is that in the environmental selection, offspring individuals are selected one by one based on a computationally efficient convergence indicator to increase the selection pressure toward the Pareto optimal front...
January 9, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28092573/environment-sensitivity-based-cooperative-co-evolutionary-algorithms-for-dynamic-multi-objective-optimization
#4
Biao Xu, Yong Zhang, Dunwei Gong, Yinan Guo, Miao Rong
Dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment...
January 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28092557/single-image-super-resolution-using-global-regression-based-on-multiple-local-linear-mappings
#5
Jae-Seok Choi, Munchurl Kim
Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition (FHD) input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between PSNR performances and computational complexity...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092554/graph-laplacian-regularization-for-image-denoising-analysis-in-the-continuous-domain
#6
Jiahao Pang, Gene Cheung
Inverse imaging problems are inherently underdetermined, and hence it is important to employ appropriate image priors for regularization. One recent popular prior- the graph Laplacian regularizer-assumes that the target pixel patch is smooth with respect to an appropriately chosen graph. However, the mechanisms and implications of imposing the graph Laplacian regularizer on the original inverse problem are not well understood. To address this problem, in this paper we interpret neighborhood graphs of pixel patches as discrete counterparts of Riemannian manifolds and perform analysis in the continuous domain, providing insights into several fundamental aspects of graph Laplacian regularization for image denoising...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092553/deep-aesthetic-quality-assessment-with-semantic-information
#7
Yueying Kao, Ran He, Kaiqi Huang
Human beings often assess the aesthetic quality of an image coupled with the identification of the image's semantic content. This paper addresses the correlation issue between automatic aesthetic quality assessment and semantic recognition. We cast the assessment problem as the main task among a multitask deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of aesthetic and semantic labels...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092552/discriminative-elastic-net-regularized-linear-regression
#8
Zheng Zhang, Zhihui Lai, Yong Xu, Ling Shao, Jian Wu, Guo-Sen Xie
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zeroone matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of theses methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092551/hierarchical-image-segmentation-based-on-iterative-contraction-and-merging
#9
Jia-Hao Syu, Sheng-Jyh Wang, Li-Chun Wang
In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging (ICM). In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092549/learning-short-binary-codes-for-large-scale-image-retrieval
#10
Li Liu, Mengyang Yu, Ling Shao
Large-scale visual information retrieval has become an active research area in this big data era. Recently, hashing/ binary coding algorithms prove to be effective for scalable retrieval applications. Most existing hashing methods require relatively long binary codes (i.e., over hundreds of bits, sometimes even thousands of bits) to achieve reasonable retrieval accuracies. However, for some realistic and unique applications such as on wearable or mobile devices, only short binary codes can be used for efficient image retrieval due to the limitation of computational resources or bandwidth on these devices...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092548/linear-spectral-clustering-superpixel
#11
Jiansheng Chen, Zhengqin Li, Bo Huang
In this paper, we present a superpixel segmentation algorithm called linear spectral clustering (LSC), which is capable of producing superpixels with both high boundary adherence and visual compactness for natural images with low computational costs. In LSC, a normalized cuts based formulation of image segmentation is adopted using a distance metric that measures both the color similarity and the space proximity between image pixels. However, rather than directly using the traditional eigen-based algorithm, we approximate the similarity metric through a deliberately designed kernel function such that pixel values can be explicitly mapped to a high-dimensional feature space...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092544/discriminative-multi-view-interactive-image-re-ranking
#12
Jun Li, Chang Xu, Wankou Yang, Changyin Sun, Dacheng Tao
-Given unreliable visual patterns and insufficient query information, content-based image retrieval (CBIR) is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose Discriminative Multi-view INTeractive Image Re-ranking (DMINTIR), which integrates User Relevance Feedback (URF) capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092543/a-robust-wrap-reduction-algorithm-for-fringe-projection-profilometry-and-applications-in-magnetic-resonance-imaging
#13
Miguel Arevalillo-Herraez, Maximo Cobos, Miguel Garcia Pineda
In this paper, we present an effective algorithm to reduce the number of wraps in a two-dimensional (2-D) phase signal provided as input. The technique is based on an accurate estimate of the fundamental frequency of a 2-D complex signal with the phase given by the input, and the removal of a dependent additive term from the phase map. Unlike existing methods based on the Discrete Fourier Transform (DFT), the frequency is computed by using noise-robust estimates that are not restricted to integer values. Then, to deal with the problem of a non-integer shift in the frequency domain, an equivalent operation is carried out on the original phase signal...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092542/bayesian-face-sketch-synthesis
#14
Nannan Wang, Xinbo Gao, Leiyu Sun, Jie Li
xemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical modelsxemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical modelsE...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092540/robust-and-discriminative-labeling-for-multi-label-active-learning-based-on-maximum-correntropy-criterion
#15
Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Dacheng Tao
Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagnosing discriminative labels. Can we reduce the label costs and improve the ability to train a good model for multi-label learning simultaneously? Active learning addresses the less training samples problem by querying the most valuable samples to achieve a better performance with little costs...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092523/longitudinal-study-of-automatic-face-recognition
#16
Lacey Best-Rowden, Anil K Jain
The two underlying premises of automatic face recognition are uniqueness and permanence. This paper investigates the permanence property by addressing the following: Does face recognition ability of state-of-the-art systems degrade with elapsed time between enrolled and query face images? If so, what is the rate of decline w.r.t. the elapsed time? While previous studies have reported degradations in accuracy, no formal statistical analysis of large-scale longitudinal data has been conducted. We conduct such an analysis on two mugshot databases, which are the largest facial aging databases studied to date in terms of number of subjects, images per subject, and elapsed times...
January 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28092522/active-self-paced-learning-for-cost-effective-and-progressive-face-identification
#17
Liang Lin, Keze Wang, Deyu Meng, Wangmeng Zuo, Lei Zhang
This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets...
January 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28092514/a-multivariate-approach-for-patient-specific-eeg-seizure-detection-using-empirical-wavelet-transform
#18
Abhijit Bhattacharyya, Ram Bilas Pachori
OBJECTIVE: This paper investigates the multivariate oscillatory nature of electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure detection. METHODS: The empirical wavelet transform (EWT) has been explored for the multivariate signals in order to determine the joint instantaneous amplitudes and frequencies in signal adaptive frequency scales. The proposed multivariate extension of EWT has been studied on multivariate multi-component synthetic signal, as well as on multivariate EEG signals of CHB-MIT scalp EEG database...
January 9, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28092510/unobtrusive-and-wearable-systems-for-automatic-dietary-monitoring
#19
Temiloluwa Prioleau, Elliot Moore, Maysam Ghovanloo
The threat of obesity, diabetes, anorexia and bulimia in our society today has motivated extensive research on dietary monitoring. Standard self-report methods such as 24-hour recall and food frequency questionnaires are expensive, burdensome and unrealiable to handle the growing health crisis. Long-term activity monitoring in daily living is a promising approach to provide individuals with quantitative feedback that can encourage healthier habits. Although several studies have attempted automating dietary monitoring using wearable, hand-held, smartobject, and environmental systems, it remains an open research problem...
January 16, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28090603/spatial-clockwork-recurrent-neural-network-for-muscle-perimysium-segmentation
#20
Yuanpu Xie, Zizhao Zhang, Manish Sapkota, Lin Yang
Accurate segmentation of perimysium plays an important role in early diagnosis of many muscle diseases because many diseases contain different perimysium inflammation. However, it remains as a challenging task due to the complex appearance of the perymisum morphology and its ambiguity to the background area. The muscle perimysium also exhibits strong structure spanned in the entire tissue, which makes it difficult for current local patch-based methods to capture this long-range context information. In this paper, we propose a novel spatial clockwork recurrent neural network (spatial CW-RNN) to address those issues...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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