Color Image Classification Using Block Matching and Learning

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概要

• 論文の詳細を見る

• In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.

• 2009-07-01

著者

• Hotta Seiji

  Tokyo Univ. Agriculture And Technol. Koganei‐shi Jpn

• Hotta Seiji

  Tokyo University Of Agriculture And Technology

• Kondo Kazuki

  Tokyo Univ. Agriculture And Technol. Koganei‐shi Jpn

• Kondo Kazuki

  Tokyo University Of Agriculture And Technology

• Hotta Seiji

  Tokyo Univ. Agriculture And Technol.

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