Class specific dictionary learning based kernel collaborative representation for fine-grained image classification | IEEE Conference Publication | IEEE Xplore

Class specific dictionary learning based kernel collaborative representation for fine-grained image classification


Abstract:

Recently, dictionary learning based sparse representation algorithm has been widely adopted and achieved satisfying performance in image classification. However, sparse r...Show More

Abstract:

Recently, dictionary learning based sparse representation algorithm has been widely adopted and achieved satisfying performance in image classification. However, sparse representation based classification (SRC) as well as collaborative representation based classification (CRC) always result in high residual error due to their basic assumption that considers training samples as dictionary directly for each category. And conventional class specific dictionary learning algorithm usually operates in the Euclidean space and fails to capture nonlinear information. To deal with these problems, we propose a classification algorithm which is called class specific dictionary learning based kernel collaborative representation (CSDL-KCRC) to enhance the classification accuracy. Extensive experimental results operated on three fine-grained image datasets, such as Oxford 102-Flowers dataset, Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset and Stanford Dogs dataset, demonstrate the effectiveness of CSDL-KCRC in image classification.
Date of Conference: 09-12 October 2016
Date Added to IEEE Xplore: 09 February 2017
ISBN Information:
Conference Location: Budapest, Hungary

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