Medical Image Classification and Retrieval Using BOF Feature Histogram with Random Forest Classifier


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 4, pp. 273-280, Apr. 2013
10.3745/KTSDE.2013.2.4.273,   PDF Download:

Abstract

This paper presents novel OCS-LBP (Oriented Center Symmetric Local Binary Patterns) based on orientation of pixel gradient and image retrieval system based on BoF (Bag-of-Feature) and random forest classifier. Feature vectors extracted from training data are clustered into code book and each feature is transformed new BoF feature using code book. BoF features are applied to random forest for training and random forest having N classes is constructed by combining several decision trees. For testing, the same OCS-LBP feature is extracted from a query image and BoF is applied to trained random forest classifier. In contrast to conventional retrieval system, query image selects similar K-nearest neighbor (K-NN) classes after random forest is performed. Then, Top K similar images are retrieved from database images that are only labeled K-NN classes. Compared with other retrieval algorithms, the proposed method Shows both fast processing time and improved retrieval performance.


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Cite this article
[IEEE Style]
S. J. Eun, K. B. Chul, N. J. Yeal, "Medical Image Classification and Retrieval Using BOF Feature Histogram with Random Forest Classifier," KIPS Transactions on Software and Data Engineering, vol. 2, no. 4, pp. 273-280, 2013. DOI: 10.3745/KTSDE.2013.2.4.273.

[ACM Style]
Son Jung Eun, Ko Byoung Chul, and Nam Jae Yeal. 2013. Medical Image Classification and Retrieval Using BOF Feature Histogram with Random Forest Classifier. KIPS Transactions on Software and Data Engineering, 2, 4, (2013), 273-280. DOI: 10.3745/KTSDE.2013.2.4.273.