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Ordinal preserving projection: a novel dimensionality reduction method for image ranking

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Published:05 June 2012Publication History

ABSTRACT

Learning to rank has been demonstrated as a powerful tool for image ranking, but the issue of the "curse of dimensionality" is a key challenge of learning a ranking model from a large image database. This paper proposes a novel dimensionality reduction algorithm named ordinal preserving projection (OPP) for learning to rank. We first define two matrices, which work in the row direction and column direction respectively. The two matrices aim at leveraging the global structure of the data set and ordinal information of the observations. By maximizing the corresponding objective functions, we can obtain two optimal projection matrices mapping original data points into low-dimensional subspace, in which both global structure and ordinal information can be preserved. The experiments are conducted on the public available MSRA-MM image data set and "Web Queries" image data set, and the experimental results demonstrate the effectiveness of the proposed method.

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          cover image ACM Conferences
          ICMR '12: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
          June 2012
          489 pages
          ISBN:9781450313292
          DOI:10.1145/2324796

          Copyright © 2012 ACM

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          Publication History

          • Published: 5 June 2012

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          ICMR '12 Paper Acceptance Rate50of145submissions,34%Overall Acceptance Rate254of830submissions,31%

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