{"title":"Parallel incremental SVM for classifying million images with very high-dimensional signatures into thousand classes","authors":"Thanh-Nghi Doan, Thanh-Nghi Do, F. Poulet","doi":"10.1109/IJCNN.2013.6707121","DOIUrl":null,"url":null,"abstract":"ImageNet dataset [1] with more than 14M images and 21K classes makes the problem of visual classification more difficult to deal with. One of the most difficult tasks is to train a fast and accurate classifier on computers with limited memory resource. In this paper, we address this challenge by extending the state-of-the-art large scale classifier Power Mean SVM (PmSVM) proposed by Jianxin Wu [2] in three ways: (1) An incremental learning for PmSVM, (2) A balanced bagging algorithm for training binary classifiers, (3) Parallelize the training process of classifiers with several multi-core computers. Our approach is evaluated on 1K classes of ImageNet (ILSVRC 1000 [3]). The evaluation shows that our approach can save up to 84.34% memory usage and the training process is 297 times faster than the original implementation and 1508 times faster than the state-of-the-art linear classifier (LIBLINEAR [4]).","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2013 International Joint Conference on Neural Networks (IJCNN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2013.6707121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
ImageNet dataset [1] with more than 14M images and 21K classes makes the problem of visual classification more difficult to deal with. One of the most difficult tasks is to train a fast and accurate classifier on computers with limited memory resource. In this paper, we address this challenge by extending the state-of-the-art large scale classifier Power Mean SVM (PmSVM) proposed by Jianxin Wu [2] in three ways: (1) An incremental learning for PmSVM, (2) A balanced bagging algorithm for training binary classifiers, (3) Parallelize the training process of classifiers with several multi-core computers. Our approach is evaluated on 1K classes of ImageNet (ILSVRC 1000 [3]). The evaluation shows that our approach can save up to 84.34% memory usage and the training process is 297 times faster than the original implementation and 1508 times faster than the state-of-the-art linear classifier (LIBLINEAR [4]).