{"title":"A comparison of active classification methods for content-based image retrieval","authors":"P. Gosselin, M. Cord","doi":"10.1145/1039470.1039483","DOIUrl":"https://doi.org/10.1145/1039470.1039483","url":null,"abstract":"This paper deals with content-based image indexing and category retrieval in general databases. Statistical learning approaches have been recently introduced in CBIR. Labelled images are considered as training data in learning strategy based on classification process. We introduce an active learning strategy to select the most difficult images to classify with only few training data. Experimentations are carried out on the COREL database. We compare seven classification strategies to evaluate the active learning contribution in CBIR.","PeriodicalId":346313,"journal":{"name":"Computer Vision meets Databases","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114578874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The PIBE personalizable image browsing engine","authors":"Ilaria Bartolini, P. Ciaccia, M. Patella","doi":"10.1145/1039470.1039482","DOIUrl":"https://doi.org/10.1145/1039470.1039482","url":null,"abstract":"In this paper we describe PIBE, a new Personalizable Image Browsing Engine that allows an effective visual exploration of large image collections combining computer vision and database techniques. The principal features of PIBE include the possibility of modifying the browsing structure by means of graphical personalization actions provided by the visual interface, and of persistently storing such customizations for subsequent browsing sections. The PIBE hierarchical browsing structure, called Browsing Tree, can be locally customized, thus avoiding global reorganizations, which are clearly unfeasible with large collections. Indeed, each node of the Browsing Tree has associated a cluster of images and a specific dissimilarity function. We present the basic principles of the PIBE engine, and report some experimental results showing the effectiveness and the efficiency of the browsing and personalization functionalities provided.","PeriodicalId":346313,"journal":{"name":"Computer Vision meets Databases","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}