A new content-based image retrieval using the multidimensional generalization of wald-wolfowitz runs test (特集 若手研究者) -- (音声画像処理・認識)

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概要

• 論文の詳細を見る

• This paper proposes two new similarity measures for the content-based image retrieval (CBIR) systems. The similarity measures are based on the k-means clustering algorithm and the multidimensional generalization of the Wald-Wolfowitz (MWW) runs test. The performance comparisons between the proposed similarity measures and a current CBIR similarity measure based on the MWW runs test were performed, and it can be seen that the proposed similarity measures outperform the current similarity measure with respect to the precision and the computational time.

• 社団法人 電気学会の論文
• 2009-01-01

著者

• Hamamoto Kazuhiko

  School Of Information And Telecommunication Engineering Tokai University

• Leauhatong Thurdsak

  Graduate School Of Science And Technology Tokai University

• ATSUTA Kiyoaki

  School of Information and Telecommunication Engineering, Tokai University

• KONDO Shozo

  School of Information and Telecommunication Engineering, Tokai University

• Kondo Shozo

  School Of Information And Telecommunication Engineering Tokai University

• Atsuta Kiyoaki

  School Of Information And Telecommunication Engineering Tokai University

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• A new coarse-to-fine method for computing disparity images by sampling disparity spaces (特集 若手研究者) -- (音声画像処理・認識)
• A new content-based image retrieval using the multidimensional generalization of wald-wolfowitz runs test (特集 若手研究者) -- (音声画像処理・認識)
• A new content-based image retrieval using the multivariate generalization of Wald-Wolfowitz runs test and the k-means clustering algorithm

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