Abstract
Recently, the visual attribute of images is becoming a research focus in computer vision and multimedia retrieval areas due to its describable or human-nameable nature for image understanding. In this paper, the visual attribute is utilized to boost the result of image ranking. To well modeling the images along with their visual attributes, hypergraph is used to integrate the visual attributes with low-level features of images. After that, we perform a ranking algorithm on the hypergraph. The experiment conducted on Animal with Attribute(AwA) dataset demonstrate the effectiveness of our proposed approach.
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Yu, Z., Tang, S., Zhang, Y., Shao, J. (2012). Image Ranking via Attribute Boosted Hypergraph. In: Lin, W., et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_73
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DOI: https://doi.org/10.1007/978-3-642-34778-8_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34777-1
Online ISBN: 978-3-642-34778-8
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