Ensemble of multiple instance classifiers for image re-ranking
Date
2014Source Title
Image and Vision Computing
Print ISSN
0262-8856
Publisher
Elsevier Ltd
Volume
32
Issue
5
Pages
348 - 362
Language
English
Type
ArticleItem Usage Stats
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Abstract
Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ranking performance. In this paper, we look into this problem of image re-ranking and propose a system that automatically constructs multiple candidate "multi-instance bags (MI-bags)", which are likely to contain relevant images. These automatically constructed bags are then utilized by ensembles of Multiple Instance Learning (MIL) classifiers and the images are re-ranked according to the final classification responses. Our method is unsupervised in the sense that, the only input to the system is the text query itself, without any user feedback or annotation. The experimental results demonstrate that constructing multiple instance bags based on the retrieval order and utilizing ensembles of MIL classifiers greatly enhance the retrieval performance, achieving on par or better results compared to the state-of-the-art. © 2014 Elsevier B.V.
Keywords
Image re-rankingImage retrieval
Multiple Instance Learning
Learning systems
Image rankings
Image re rankings
Multiple instance learning
Multiple instances
Retrieval performance
Text-based image retrievals
User feedback
Visual content
Image retrieval