Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment.

ABSTRACT: An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. The support vector machine (SVM) classifier is then used to perform parallel training to obtain the optimal SVM classification model, which is then tested. The Pascal VOC 2012, Caltech 256 and SUN databases are adopted to build a massive image library. The speedup, classification accuracy and training time are te

SUBMITTER: Cao J 

PROVIDER: S-EPMC6457544 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature