Based on Semi-Supervised Clustering with the Boost Similarity Metric Method for Face Retrieval

Abstract:

The focus of this paper is on Metric Learning, with particular interest in incorporating side information to make it semi-supervised. This study is primarily motivated by an application: face-image clustering. In the paper introduces metric learning and semi-supervised clustering, Boost the similarity metric learning method that adapt the underlying similarity metric used by the clustering algorithm. we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called Boost the Similarity Metric Method for Face Retrieval, Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms. This paper followed by the discussion of experiments on face-image clustering.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 2720-2723
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.2720
• Online since: March 2014
• Authors: Wei Qiu*
• Keywords: Face Image Retrieval, Semi-Supervised Clustering, Similarity Metric Method

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