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. © 2012 Springer-Verlag.
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CITATION STYLE
Yu, Z., Tang, S., Zhang, Y., & Shao, J. (2012). Image ranking via attribute boosted hypergraph. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7674 LNCS, pp. 779–789). https://doi.org/10.1007/978-3-642-34778-8_73
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