CAJ | 학술논문

针对干果图像信息量大、分类精度低和耗时多的特点,提出利用Bag of Words模型提取图片的代表特征,并采用朴素贝叶斯分类器指导特征矩阵分类.结果表明,图像分类精度能达到80％,分类处理时间约为2 s.通过增加学习样本来进一步提高分类精度,将Bagof Words应用于干果图像识别和分类是可行的.
침대간과도상신식량대、분류정도저화모시다적특점,제출이용Bag of Words모형제취도편적대표특정,병채용박소패협사분류기지도특정구진분류.결과표명,도상분류정도능체도80％,분류처리시간약위2 s.통과증가학습양본래진일보제고분류정도,장Bagof Words응용우간과도상식별화분류시가행적.
According to the characteristics of digital dried fruit image classification which have lots of information,weaken classification accuracy and more time-consuming,it is put forward to extract image representation using the Bag-of-Words model and to classify the feature matrix with Na(i)ve Bayes Classifier.The results showed that the accuracy was over 80％,the treatment time was 2 seconds.By increasing the learning samples to further improve the classification accuracy,the Bag of Words applied to the dried fruit image recognition and classification is feasible.