Color Image Classification Using Block Matching and Learning

Kazuki KONDO  Seiji HOTTA  

IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.7   pp.1484-1487
Publication Date: 2009/07/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.1484
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
color image,  block matching,  learning vector quantization,  

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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.