Abstract
Currently, web image search is mostly based on textual information associated with Web pages. However, this search method ignores visual content information of images. Visual search reranking that aims to improve the text-based image search results by leveraging visual content analysis. This paper presents a semi-supervised learning to rank framework based on Ranking SVM. To better compute the similarity between images, a new similarity measure algorithm named SM-PCA, which is relying on Principle Component Analysis, is proposed and introduced into semi-supervised learning. Finally, the proposed method is evaluated by image database downloaded from the popular image search engine. Experiments show that our method outperforms the state-of-the-art method.
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Zhang, J., Jing, P., Ji, Z., Su, Y. (2011). Image Search Reranking with Transductive Learning to Rank Framework. In: Liu, C., Chang, J., Yang, A. (eds) Information Computing and Applications. ICICA 2011. Communications in Computer and Information Science, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27452-7_72
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DOI: https://doi.org/10.1007/978-3-642-27452-7_72
Publisher Name: Springer, Berlin, Heidelberg
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