An efficient image aesthetic analysis system using Hadoop

Wang, Weining; Zhao, Weijian; Cai, Chengjia; Huang, Jiexiong; Xu, Xiangmin; Li, Lei

doi:10.1016/j.image.2015.07.006

Cited by 17 publications

(2 citation statements)

References 31 publications

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“…On top of HDFS is the MapReduce engine, which consists of JobTrackers and TaskTrackers two parts. Hadoop's MapReduce is a parallel computing framework that consists of two phases: Map and Reduce [ 22 , 23 ]. In the Map phase, a single task is broken and these task pieces are sent to multiple nodes, which are then loaded into a data warehouse as a single data set in the reduce phase.…”

Section: Related Workmentioning

confidence: 99%

TCM Constitution Analysis Method Based on Parallel FP-Growth Algorithm in Hadoop Framework

Lv²,

Liu

et al. 2022

Journal of Healthcare Engineering

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This work is devoted to establishing a comparatively accurate classification model between symptoms, constitutions, and regimens for traditional Chinese medicine (TCM) constitution analysis to provide preliminary screening and decision support for clinical diagnosis. However, for the analysis of massive distributed medical data in a cloud platform, the traditional data mining methods have the problems of low mining efficiency and large memory consumption, and long tuning time, an association rules method for TCM constitution analysis (ARA-TCM) is proposed that based on FP-growth algorithm and the open-source distributed file system in Hadoop framework (HDFS) to make full use of its powerful parallel processing capability. Firstly, the proposed method was used to explore the association rules between the 9 kinds of TCM constitutions and symptoms, as well as the regimen treatment plans, so as to discover the rules of typical clinical symptoms and treatment rules of different constitutions and to conduct an evidence-based medical evaluation of TCM effects in constitution-related chronic disease health management. Secondly, experiments were applied on a self-built TCM clinical records database with a total of 30,071 entries and it is found that the top three constitutions are mid constitution (42.3%), hot and humid constitution (31.3%), and inherited special constitution (26.2%), respectively. What is more, there are obvious promotions in the precision and recall rate compared with the Apriori algorithm, which indicates that the proposed method is suitable for the classification of TCM constitutions. This work is mainly focused on uncovering the rules of “disease symptoms constitution regimen” in TCM medical records, but tongue image and pulse signal are also very important to TCM constitution analysis. Therefore, this additional information should be considered into further studies to be more in line with the actual clinical needs.

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Section: Related Workmentioning

confidence: 99%

TCM Constitution Analysis Method Based on Parallel FP-Growth Algorithm in Hadoop Framework

Lv²,

Liu

et al. 2022

Journal of Healthcare Engineering

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“…Apart from the aesthetic perception studies by psychological research method, the image aesthetic can be studied by subjective visual attributes analysis [40][41][42][43][44]. The image aesthetic evaluation method can provide the basic method and empirical research procedure [45][46][47][48][49][50], which can be utilized as references in product aesthetic evaluation. Machine learning algorithms, especially deep learning networks, were widely used in image aesthetic score prediction and aesthetic quality classification [51][52][53][54].…”

Section: Image Aesthetic Modelingmentioning

confidence: 99%

Product Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNs

Xing

et al. 2020

IEEE Access

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A visual aesthetic is a crucial determinant of product design evaluation. Through the analysis of image features, not only can we evaluate the aesthetic level, but also we can reveal the whole quality of the design proposal. We assume that it could be a potential pattern to predict the ultimate success of the proposal in product design that a visual aesthetic can be a cue for award classification modeling. Consequently, we conduct investigation on a dataset of over 10,003 design submissions in a design competition held once a year from 2008 to 2018 in order to manifest the assumption. Due to the remarkable performance of deep convolutional neural networks (DCNNs), we compare seven deep learning methods to explore an optimal model for design award prediction based on product image analysis. The result of the experiments indicates that the proposed method achieves comparative accuracy in design award classification result predication, with the optimal classification accuracy of 70.79% using the SEFL-ResNet (Squeeze and Excitation-Focal Loss-ResNet) method.

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2023

Aesthetics in Digital Photography

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An efficient image aesthetic analysis system using Hadoop

Cited by 17 publications

References 31 publications

TCM Constitution Analysis Method Based on Parallel FP-Growth Algorithm in Hadoop Framework

TCM Constitution Analysis Method Based on Parallel FP-Growth Algorithm in Hadoop Framework

Product Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNs

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