Please use this identifier to cite or link to this item:
Type: Artigo de evento
Title: Semi-supervised Learning For Relevance Feedback On Image Retrieval Tasks
Author: Pedronette D.C.G.
Calumby R.T.
Da S. Torres R.
Abstract: Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.
Editor: IEEE Computer Society
Citation: Brazilian Symposium Of Computer Graphic And Image Processing. Ieee Computer Society, v. , n. , p. 243 - 250, 2014.
Rights: fechado
Identifier DOI: 10.1109/SIBGRAPI.2014.44
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-84907972875.pdf323.44 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.