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Visual Odometry Based Omni-directional Hyperlapse Read chapter Read first chapter

2018 | OriginalPaper | Chapter

MKL Based Local Label Diffusion for Automatic Image Annotation

Authors : Abhijeet Kumar, Anjali Anil Shenoy, Avinash Sharma

Published in: Computer Vision, Pattern Recognition, Image Processing, and Graphics

Publisher: Springer Singapore

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Abstract

The task of automatic image annotation attempts to predict a set of semantic labels for an image. Majority of the existing methods discover a common latent space that combines content and semantic image similarity using the metric learning kind of global learning framework. This limits their applicability to large datasets. On the other hand, there are few methods which entirely focus on learning a local latent space for every test image. However, they completely ignore the global structure of the data. In this work, we propose a novel image annotation method which attempts to combine best of both local and global learning methods. We introduce the notion of neighborhood-types based on the hypothesis that similar images in content/feature space should also have overlapping neighborhoods. We also use graph diffusion as a mechanism for label transfer. Experiments on publicly available datasets show promising performance.

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Footnotes
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Metadata
Title
MKL Based Local Label Diffusion for Automatic Image Annotation
Authors
Abhijeet Kumar
Anjali Anil Shenoy
Avinash Sharma
Copyright Year
2018
Publisher
Springer Singapore
Book
Computer Vision, Pattern Recognition, Image Processing, and Graphics

Print ISBN: 978-981-13-0019-6

Electronic ISBN: 978-981-13-0020-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-13-0020-2_34

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