Color Image Classification Using Block Image Replacement and Local Averaging Classifier(Internationa Session 7)

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概要

• 論文の詳細を見る

• In this paper, we propose a color image classification method using block image replacement and a local averaging classifier (LAC). First, an input image and training ones are partitioned into block images. The k-nearest blocks of each block of the input image are selected from the blocks of training images in individual classes. Next, each block of the input image is replaced with the mean block computed with its selected k-nearest blocks. Finally, the proposed method outputs the class that has the minimum distance between the block-replaced image and the input one. In the proposed method, the number of blocks tends to be large, so reduction of blocks with modified learning vector quantization is presented in this paper. The performance of the proposed method is verified with experiments on the WANG color image dataset.

• 社団法人電子情報通信学会の論文
• 2007-10-18

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

• Hotta Seiji

  Tokyo Univ. Agriculture And Technol.

関連論文

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• Color Image Classification Using Block Image Replacement and Local Averaging Classifier(Internationa Session 7)
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