Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84200
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Type: Conference paper
Title: Formulating semantic image annotation as a supervised learning problem
Author: Carneiro, G.
Vasconcelos, N.
Citation: Proceedings, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2 / C. Schmid, S. Soatto, and C. Tomasi (eds.): pp.163-168
Publisher: IEEE
Publisher Place: USA
Issue Date: 2005
Series/Report no.: PROCEEDINGS - IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION
ISBN: 0769523722
9780769523729
ISSN: 1063-6919
Conference Name: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (18th : 2005 : San Diego, CA, U.S.A.)
Statement of
Responsibility: 
Gustavo Carneiro, Nuno Vasconcelos
Abstract: We introduce a new method to automatically annotate and retrieve images using a vocabulary of image semantics. The novel contributions include a discriminant formulation of the problem, a multiple instance learning solution that enables the estimation of concept probability distributions without prior image segmentation, and a hierarchical description of the density of each image class that enables very efficient training. Compared to current methods of image annotation and retrieval, the one now proposed has significantly smaller time complexity and better recognition performance. Specifically, its recognition complexity is O(C×R), where C is the number of classes (or image annotations) and R is the number of image regions, while the best results in the literature have complexity O(T×R), where T is the number of training images. Since the number of classes grows substantially slower than that of training images, the proposed method scales better during training, and processes test images faster This is illustrated through comparisons in terms of complexity, time, and recognition performance with current state-of-the-art methods.
Rights: Copyright © 2005 by The Institute of Electrical and Electronics Engineers, Inc.
RMID: 0020114260
DOI: 10.1109/CVPR.2005.164
Appears in Collections:Computer Science publications

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