C. Almeida, R. Souza, C. Rodrigues, N. L. C. Junior
{"title":"Image Retrieval Using the Curvature Scale Space (CSS) Technique and the Self-Organizing Map (SOM) Model under Affine Transforms","authors":"C. Almeida, R. Souza, C. Rodrigues, N. L. C. Junior","doi":"10.1109/HIS.2007.19","DOIUrl":null,"url":null,"abstract":"In a previous work [1], we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation under affine transforms. Moreover, the CSS images extracted from a database are processed and described by median vectors that constitutes the training data set for a SOM neural network. This way of description improves the accuracy of image retrieval in comparison with the previous work [1] that used the first principal component of the PCA technique. Experiments with a benchmark database are carried out to demonstrate the usefulness of the proposed methodology.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"229 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HIS.2007.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In a previous work [1], we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation under affine transforms. Moreover, the CSS images extracted from a database are processed and described by median vectors that constitutes the training data set for a SOM neural network. This way of description improves the accuracy of image retrieval in comparison with the previous work [1] that used the first principal component of the PCA technique. Experiments with a benchmark database are carried out to demonstrate the usefulness of the proposed methodology.