By Topic

Multiple instance learning using visual phrases for object classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Yan Song ; Dept. of EEIS, Univ. of Sci. & Technol. of China, Hefei, China ; Qi Tian ; Mengyue Wang ; Heng Liu
more authors

Recently, bag of words (BoW) model has led to many significant results in visual object classification. However, due to the limited descriptive and discriminative ability of visual words, the resulting performance of visual object classification is still incomparable to its analogy in text domain, i.e. document categorization. Furthermore, for weakly labeled image data, where we only know whether an object is present or not, traditional learning based methods may suffer from background clutters and large appearance variations. To address these issues, we propose a novel visual phrase based Multiple Instance Learning (MIL) method. In this method, the visual phrase is first generated from over-segmented image regions of homogeneous appearance and visual words within each region, which may provide enhanced descriptive ability by enforcing the spatial coherency. Then a MIL algorithm is applied to efficiently learn from the weakly labeled image data. The experiments on benchmark datasets show that our proposed method always significantly outperforms several state-of-the-art algorithms, such as Spatial Pyramid Matching (SPM) and Spatial-LTM.

Published in:

Multimedia and Expo (ICME), 2010 IEEE International Conference on

Date of Conference:

19-23 July 2010