Deep dictionary learning for fine-grained image classification

M. Srinivas, Yen Yu Lin, Hong Yuan Mark Liao

研究成果: Chapter同行評審

13 引文 斯高帕斯(Scopus)


Fine-grained image classification is quite challenging due to high inter-class similarity and large intra-class variations. Another issue is the small amount of training images with a large number of classes to be identified. To address the chal-lenges, we propose a model for fine-grained image classifi-cation with its application to bird species recognition. Based on the features extracted by bilinear convolutional neural network (BCNN), we propose an on-line dictionary learn-ing algorithm where the principle of sparsity is integrated into classification. The features extracted by BCNN encode pairwise neuron interaction in a translation-invariant manner. This property is valuable to fine-grained classification. The proposed algorithm for dictionary learning further carries out sparsity based classification, where training data can be rep-resented with a less number of dictionary atoms. It alleviates the problems caused by insufficient training data, and makes classification much more efficient. Our approach is evaluated and compared with the state-of-the-art approaches on the CUB-200-2011 dataset. The promising experimental results demonstrate its efficacy and superiority.
原文American English
主出版物標題2017 IEEE International Conference on Image Processing (ICIP)
發行者IEEE Computer Society
出版狀態Published - 20 2月 2018


名字Proceedings - International Conference on Image Processing, ICIP


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