Multi-instance multi-label learning for image classification with large vocabularies

Oksana Yakhnenko, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


Multiple Instance Multiple Label learning problem has received much attention in machine learning and computer vision literature due to its applications in image classification and object detection. However, the current state-of-the-art solutions to this problem lack scalability and cannot be applied to datasets with a large number of instances and a large number of labels. In this paper we present a novel learning algorithm for Multiple Instance Multiple Label learning that is scalable for large datasets and performs comparable to the state-of-the-art algorithms. The proposed algorithm trains a set of discriminative multiple instance classifiers (one for each label in the vocabulary of all possible labels) and models the correlations among labels by finding a low rank weight matrix thus forcing the classifiers to share weights. This algorithm is a linear model unlike the state-of-the-art kernel methods which need to compute the kernel matrix. The model parameters are efficiently learned by solving an unconstrained optimization problem for which Stochastic Gradient Descent can be used to avoid storing all the data in memory.

Original languageEnglish (US)
Title of host publicationBMVC 2011 - Proceedings of the British Machine Vision Conference 2011
PublisherBritish Machine Vision Association, BMVA
StatePublished - 2011
Event2011 22nd British Machine Vision Conference, BMVC 2011 - Dundee, United Kingdom
Duration: Aug 29 2011Sep 2 2011


Other2011 22nd British Machine Vision Conference, BMVC 2011
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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