{"title":"作者检索中梯度描述符的评价与不相似学习","authors":"Mohamed Lamine Bouibed, H. Nemmour, Y. Chibani","doi":"10.1109/ICIST.2018.8426179","DOIUrl":null,"url":null,"abstract":"In order to take advantage from collections of digitized handwritten documents, effective indexing and retrieval techniques are required. This work focuses on automatic writer retrieval, which is the task of finding in a dataset, all documents written by the same person. Contrary to conventional writer retrieval techniques that are based on dissimilarity measures, we propose to use the SVM classifier to perform the retrieval task. First, local gradient features are used to generate handwritten features. Then, dissimilarities calculated between intra-writer and inter-writer documents are used to train a SVM to allow an automatic retrieval of all the writers documents. Experiments are conducted on CVL and ICDAR 2011 datasets. The performance evaluation of the proposed system is carried out comparatively to the cosine similarity. Results obtained evince a significant improvement offered by SVM, which gives comparable and sometimes better scores than the state of the art.","PeriodicalId":331555,"journal":{"name":"2018 Eighth International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Evaluation of Gradient Descriptors and Dissimilarity Learning for Writer Retrieval\",\"authors\":\"Mohamed Lamine Bouibed, H. Nemmour, Y. Chibani\",\"doi\":\"10.1109/ICIST.2018.8426179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to take advantage from collections of digitized handwritten documents, effective indexing and retrieval techniques are required. This work focuses on automatic writer retrieval, which is the task of finding in a dataset, all documents written by the same person. Contrary to conventional writer retrieval techniques that are based on dissimilarity measures, we propose to use the SVM classifier to perform the retrieval task. First, local gradient features are used to generate handwritten features. Then, dissimilarities calculated between intra-writer and inter-writer documents are used to train a SVM to allow an automatic retrieval of all the writers documents. Experiments are conducted on CVL and ICDAR 2011 datasets. The performance evaluation of the proposed system is carried out comparatively to the cosine similarity. Results obtained evince a significant improvement offered by SVM, which gives comparable and sometimes better scores than the state of the art.\",\"PeriodicalId\":331555,\"journal\":{\"name\":\"2018 Eighth International Conference on Information Science and Technology (ICIST)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Eighth International Conference on Information Science and Technology (ICIST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIST.2018.8426179\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Eighth International Conference on Information Science and Technology (ICIST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIST.2018.8426179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluation of Gradient Descriptors and Dissimilarity Learning for Writer Retrieval
In order to take advantage from collections of digitized handwritten documents, effective indexing and retrieval techniques are required. This work focuses on automatic writer retrieval, which is the task of finding in a dataset, all documents written by the same person. Contrary to conventional writer retrieval techniques that are based on dissimilarity measures, we propose to use the SVM classifier to perform the retrieval task. First, local gradient features are used to generate handwritten features. Then, dissimilarities calculated between intra-writer and inter-writer documents are used to train a SVM to allow an automatic retrieval of all the writers documents. Experiments are conducted on CVL and ICDAR 2011 datasets. The performance evaluation of the proposed system is carried out comparatively to the cosine similarity. Results obtained evince a significant improvement offered by SVM, which gives comparable and sometimes better scores than the state of the art.