scholarly journals Multifeature Extreme Ordinal Ranking Machine for Facial Age Estimation

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Zhao ◽  
Han Wang ◽  
Guang-Bin Huang
Keyword(s):  
Age Estimation ◽  
Optimization Problems ◽  
State Of The Art ◽  
Mathematical Optimization ◽  
The State ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Training Process ◽  
Ordinal Ranking ◽  
Facial Age

Recently the state-of-the-art facial age estimation methods are almost originated from solving complicated mathematical optimization problems and thus consume huge quantities of time in the training process. To refrain from such algorithm complexity while maintaining a high estimation accuracy, we propose a multifeature extreme ordinal ranking machine (MFEORM) for facial age estimation. Experimental results clearly demonstrate that the proposed approach can sharply reduce the runtime (even up to nearly one hundred times faster) while achieving comparable or better estimation performances than the state-of-the-art approaches. The inner properties of MFEORM are further explored with more advantages.

scholarly journals Deeply-learned Hybrid Representations for Facial Age Estimation

2019 ◽  
Author(s):  
Zichang Tan ◽  
Yang Yang ◽  
Jun Wan ◽  
Guodong Guo ◽  
Stan Z. Li
Keyword(s):  
Age Estimation ◽  
State Of The Art ◽  
The State ◽  
Complementary Information ◽  
Benchmark Datasets ◽  
Independent Features ◽  
Age Range ◽  
Facial Age

In this paper, we propose a novel unified network named Deep Hybrid-Aligned Architecture for facial age estimation. It contains global, local and global-local branches. They are jointly optimized and thus can capture multiple types of features with complementary information. In each branch, we employ a separate loss for each sub-network to extract the independent features and use a recurrent fusion to explore correlations among those region features. Considering that the pose variations may lead to misalignment in different regions, we design an Aligned Region Pooling operation to generate aligned region features. Moreover, a new large age dataset named Web-FaceAge owning more than 120K samples is collected under diverse scenes and spanning a large age range. Experiments on five age benchmark datasets, including Web-FaceAge, Morph, FG-NET, CACD and Chalearn LAP 2015, show that the proposed method outperforms the state-of-the-art approaches significantly.

scholarly journals AsymDPOP: Complete Inference for Asymmetric Distributed Constraint Optimization Problems

2019 ◽  
Author(s):  
Yanchen Deng ◽  
Ziyu Chen ◽  
Dingding Chen ◽  
Wenxin Zhang ◽  
Xingqiong Jiang
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
The State ◽  
Constraint Optimization ◽  
Memory Consumption ◽  
Distributed Constraint Optimization ◽  
Constraint Optimization Problems

Asymmetric distributed constraint optimization problems (ADCOPs) are an emerging model for coordinating agents with personal preferences. However, the existing inference-based complete algorithms which use local eliminations cannot be applied to ADCOPs, as the parent agents are required to transfer their private functions to their children. Rather than disclosing private functions explicitly to facilitate local eliminations, we solve the problem by enforcing delayed eliminations and propose AsymDPOP, the first inference-based complete algorithm for ADCOPs. To solve the severe scalability problems incurred by delayed eliminations, we propose to reduce the memory consumption by propagating a set of smaller utility tables instead of a joint utility table, and to reduce the computation efforts by sequential optimizations instead of joint optimizations. The empirical evaluation indicates that AsymDPOP significantly outperforms the state-of-the-art, as well as the vanilla DPOP with PEAV formulation.

scholarly journals Variable neighborhood search for minimum linear arrangement problem

2016 ◽  
Vol 26 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Nenad Mladenovic ◽  
Dragan Urosevic ◽  
Dionisio Pérez-Brito
Keyword(s):  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
State Of The Art ◽  
The State ◽  
Computational Experiments ◽  
Neighborhood Search ◽  
Np Hard ◽  
Linear Arrangement ◽  
Arrangement Problem ◽  
Linear Arrangement Problem

The minimum linear arrangement problem is widely used and studied in many practical and theoretical applications. It consists of finding an embedding of the nodes of a graph on the line such that the sum of the resulting edge lengths is minimized. This problem is one among the classical NP-hard optimization problems and therefore there has been extensive research on exact and approximative algorithms. In this paper we present an implementation of a variable neighborhood search (VNS) for solving minimum linear arrangement problem. We use Skewed general VNS scheme that appeared to be successful in solving some recent optimization problems on graphs. Based on computational experiments, we argue that our approach is comparable with the state-of-the-art heuristic.

scholarly journals Age Label Distribution Learning Based on Unsupervised Comparisons of Faces

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Qiyuan Li ◽  
Zongyong Deng ◽  
Weichang Xu ◽  
Zhendong Li ◽  
Hao Liu
Keyword(s):  
Age Estimation ◽  
State Of The Art ◽  
Processing Method ◽  
Wireless Sensor ◽  
Learning Method ◽  
Significant Progress ◽  
Meaningful Information ◽  
Label Distribution Learning ◽  
Label Distribution ◽  
Facial Age

Although label distribution learning has made significant progress in the field of face age estimation, unsupervised learning has not been widely adopted and is still an important and challenging task. In this work, we propose an unsupervised contrastive label distribution learning method (UCLD) for facial age estimation. This method is helpful to extract semantic and meaningful information of raw faces with preserving high-order correlation between adjacent ages. Similar to the processing method of wireless sensor network, we designed the ConAge network with the contrast learning method. As a result, our model maximizes the similarity of positive samples by data enhancement and simultaneously pushes the clusters of negative samples apart. Compared to state-of-the-art methods, we achieve compelling results on the widely used benchmark, i.e., MORPH.

Randomized error removal for online spread estimation in data streaming

2021 ◽  
Vol 14 (6) ◽  
pp. 1040-1052
Author(s):  
Haibo Wang ◽  
Chaoyi Ma ◽  
Olufemi O Odegbile ◽  
Shigang Chen ◽  
Jih-Kwon Peir
Keyword(s):  
Data Stream ◽  
State Of The Art ◽  
The State ◽  
High Rate ◽  
Estimation Accuracy ◽  
Data Streaming ◽  
Real World Data ◽  
Practical Applications ◽  
Spread Estimation ◽  
Error Removal

Measuring flow spread in real time from large, high-rate data streams has numerous practical applications, where a data stream is modeled as a sequence of data items from different flows and the spread of a flow is the number of distinct items in the flow. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a data stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs that incur much smaller processing overhead and query overhead than the state of the art, yet achieves significant accuracy improvement in spread estimation. We formally analyze the performance of these new designs. We implement them in both hardware and software, and use real-world data traces to evaluate their performance in comparison with the state of the art. The experimental results show that our best sketch significantly improves over the best existing work in terms of estimation accuracy, data item processing throughput, and online query throughput.

A Parallel Framework of Combining Satisfiability Modulo Theory with Indicator-Based Evolutionary Algorithm for Configuring Large and Real Software Product Lines

2019 ◽  
Vol 29 (04) ◽  
pp. 489-513
Author(s):  
Kai Shi ◽  
Huiqun Yu ◽  
Jianmei Guo ◽  
Guisheng Fan ◽  
Liqiong Chen ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
State Of The Art ◽  
Time Budget ◽  
Software Product Lines ◽  
The State ◽  
Product Lines ◽  
Software Product ◽  
Series Of Experiments ◽  
Optimization Efficiency

Multi-objective evolutionary algorithm (MOEA) has been widely applied to software product lines (SPLs) for addressing the configuration optimization problems. For example, the state-of-the-art SMTIBEA algorithm extends the constraint expressiveness and supports richer constraints to better address these problems. However, it just works better than the competitor for four out of five SPLs in five objectives and the convergence speed is not significantly increased for largest Linux SPL from 5 to 30[Formula: see text]min. To further improve the optimization efficiency, we propose a parallel framework SMTPORT, which combines four corresponding SMTIBEA variants and performs these variants by utilizing parallelization techniques within the limited time budget. For case studies in LVAT repository, we conduct a series of experiments on seven real-world and highly-constrained SPLs. Empirical results demonstrate that our approach significantly outperforms the state-of-the-art for all the seven SPLs in terms of a quality Hypervolume metric and a diversity Pareto Front Size indicator.

scholarly journals Rock Segmentation in the Navigation Vision of the Planetary Rovers

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3048
Author(s):  
Boyu Kuang ◽  
Mariusz Wisniewski ◽  
Zeeshan A. Rana ◽  
Yifan Zhao
Keyword(s):  
Real Time ◽  
Mean Squared Error ◽  
State Of The Art ◽  
Real Life ◽  
Visual Navigation ◽  
The State ◽  
Training Process ◽  
Planetary Rover ◽  
Transfer Training ◽  
Segmentation Framework

Visual navigation is an essential part of planetary rover autonomy. Rock segmentation emerged as an important interdisciplinary topic among image processing, robotics, and mathematical modeling. Rock segmentation is a challenging topic for rover autonomy because of the high computational consumption, real-time requirement, and annotation difficulty. This research proposes a rock segmentation framework and a rock segmentation network (NI-U-Net++) to aid with the visual navigation of rovers. The framework consists of two stages: the pre-training process and the transfer-training process. The pre-training process applies the synthetic algorithm to generate the synthetic images; then, it uses the generated images to pre-train NI-U-Net++. The synthetic algorithm increases the size of the image dataset and provides pixel-level masks—both of which are challenges with machine learning tasks. The pre-training process accomplishes the state-of-the-art compared with the related studies, which achieved an accuracy, intersection over union (IoU), Dice score, and root mean squared error (RMSE) of 99.41%, 0.8991, 0.9459, and 0.0775, respectively. The transfer-training process fine-tunes the pre-trained NI-U-Net++ using the real-life images, which achieved an accuracy, IoU, Dice score, and RMSE of 99.58%, 0.7476, 0.8556, and 0.0557, respectively. Finally, the transfer-trained NI-U-Net++ is integrated into a planetary rover navigation vision and achieves a real-time performance of 32.57 frames per second (or the inference time is 0.0307 s per frame). The framework only manually annotates about 8% (183 images) of the 2250 images in the navigation vision, which is a labor-saving solution for rock segmentation tasks. The proposed rock segmentation framework and NI-U-Net++ improve the performance of the state-of-the-art models. The synthetic algorithm improves the process of creating valid data for the challenge of rock segmentation. All source codes, datasets, and trained models of this research are openly available in Cranfield Online Research Data (CORD).

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