[1]王 敏,陈立潮,曹建芳,等.Hadoop 下自适应随机权值多特征融合图像分类[J].计算机技术与发展,2018,28(11):30-34.[doi:10.3969/ j. issn.1673-629X.2018.11.007]
　WANG Min,CHEN Li-chao,CAO Jian-fang,et al.Multi-feature Fusion Image Classification of Adaptive Random Weight Based on Hadoop[J].,2018,28(11):30-34.[doi:10.3969/ j. issn.1673-629X.2018.11.007]
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Hadoop 下自适应随机权值多特征融合图像分类()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年11期
页码:
30-34
栏目:
智能、算法、系统工程
出版日期:
2018-11-10

文章信息/Info

Title:
Multi-feature Fusion Image Classification of Adaptive Random Weight Based on Hadoop
文章编号:
1673-629X(2018)11-0030-05
作者:
王 敏1; 陈立潮1; 曹建芳2; 潘理虎1
1. 太原科技大学 计算机科学与技术学院,山西 太原 030024; 2. 忻州师范学院 计算机科学与技术系,山西 忻州 034000
Author(s):
WANG Min1; CHEN Li-chao1; CAO Jian-fang2 ; PAN Li-hu1
1. School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China; 2. Department of Computer Science and Technology,Xinzhou Teachers’ University,Xinzhou 034000,China
关键词:
自适应随机权值; 特征融合; 支持向量机; 图像分类; Hadoop 平台
Keywords:
adaptive random weights; feature fusion; support vector machine; image classification; Hadoop platform
分类号:
TP391
DOI:
10.3969/ j. issn.1673-629X.2018.11.007
文献标志码:
A
摘要:
图像具有丰富的语义信息,面对越来越复杂的图像场景,单一特征往往不能准确描述图像内容,因此多特征融合的方式在描述图像中得到了广泛应用。在融合过程中,针对准确确定权值的问题,提出一种自适应的基于随机权值的多特征融合图像分类算法。 首先生成随机权值矩阵,然后利用自定义的融合公式,得到融合特征矩阵。为验证算法的效果,将融合后的特征输入 SVM,通过 MapReduce 框架的 Map 过程和 Reduce 过程得到最优权值组合。 在 Corel1000 数据集上的实验结果表明,与单特征、1∶1∶1融合等相比,该算法分类正确率高、运行耗时少,当训练 SVM 的个数达到120 时,系统加速比几乎呈线性增长的趋势,验证了 Hadoop 平台应对高复杂性算法时的有效性。
Abstract:
Images possess rich semantic information. In the face of increasingly complex image scenes,single features often cannot accurately describe the image content. Therefore,the method of multi-feature fusion has been widely used in the description of images. In the process of fusion,we propose an adaptive multi-feature fusion image classification algorithm based on random weights to accurately determine the weight. Firstly,it generates a random weight matrix and then obtains a fusion feature matrix by using a self-defined fusion formula. To verify the effect of the algorithm,the fused features are input into SVM to gain the optimal weight combination through the Map process of MapReduce framework and Reduce process. Experiment on the Corel1000 dataset shows that the proposed algorithm has the advantages of high classification accuracy and low running time compared with the single feature,1∶1 ∶ 1 fusion and so on. When the number of SVM training reaches 120,the speed-up ratio of the system almost will present linear tendency,which verifies the effectiveness of the Hadoop platform in dealing with high complexity algorithms.

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更新日期/Last Update: 2018-11-10