Title

Semi-Supervised Dimensionality Reduction In Image Feature Space

Keywords

Dimensionality reduction; Orthogonal projection; Pairwise constraints; Semi-supervised learning

Abstract

Image feature space is typically complex due to the high dimensionality of data. Effective handling of this space has prompted many research efforts in the study of dimensionality reduction in the image domain. In this paper, we propose a semi-supervised reduction method that leverages relevance feedback information in the retrieval process to learn suitable linear and orthogonal embeddings. In the reduced space constructed by the proposed embedding, relevant images are kept close to each other, while irrelevant ones are dispersed far apart. The experimental results demonstrate the superiority of our method. Copyright 2008 ACM.

Publication Date

12-1-2008

Publication Title

Proceedings of the ACM Symposium on Applied Computing

Number of Pages

1207-1211

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1363686.1363966

Socpus ID

56749179316 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/56749179316

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