HKUST Library Institutional Repository Banner

HKUST Institutional Repository >
Computer Science and Engineering >
CSE Conference Papers >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6813
Title: Online multiple instance learning with no regret
Authors: Li, Mu
Kwok, James Tin-Yau
Lu, Bao-Liang
Keywords: Batch MI algorithm
Image classification
Object detection
Object tracking
Online multiple instance learning
Issue Date: Jun-2010
Citation: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13-18 June 2010, San Francisco, CA, USA, p. 1395-1401
Abstract: Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been used in a wide range of applications including image classification, object detection and object tracking. Typically, MI algorithms are trained in a batch setting in which the whole training set has to be available before training starts. However, in applications such as tracking, the classifier needs to be trained continuously as new frames arrive. Motivated by the empirical success of a batch MI algorithm called MILES, we propose in this paper an online MI learning algorithm that has an efficient online update procedure and also performs joint feature selection and classification as MILES. Besides, while existing online MI algorithms lack theoretical properties, we prove that the proposed online algorithm has a (cumulative) regret of O(√T), where T is the number of iterations. In other words, the average regret goes to zero asymptotically and it thus achieves the same performance as the best solution in hindsight. Experiments on a number of MI classification and object tracking data sets demonstrate encouraging results.
Rights: © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
URI: http://hdl.handle.net/1783.1/6813
Appears in Collections:CSE Conference Papers

Files in This Item:

File Description SizeFormat
05539805.pdf2137KbAdobe PDFView/Open

All items in this Repository are protected by copyright, with all rights reserved.